# Joint semi-supervised and contrastive learning enables zero-shot domain-
adaptation and multi-domain segmentation

Alvaro Gomariz1∗, Yusuke Kikuchi2, Yun Yvonna Li1, Thomas Albrecht1, Andreas
Maunz1, Daniela Ferrara2,
Huanxiang Lu1, Orcun Goksel3
1F Hoffmann-La Roche AG, Basel, Switzerland
2Genentech Inc, California, United States
3Department of Information Technology, Uppsala University, Uppsala, Sweden

###### Abstract

Despite their effectiveness, current deep learning models face challenges with
images coming from different domains with varying appearance and content. We
introduce SegCLR, a versatile framework designed to segment volumetric images
across different domains, employing supervised and contrastive learning
simultaneously to effectively learn from both labeled and unlabeled data. We
demonstrate the superior performance of SegCLR through a comprehensive
evaluation involving three diverse clinical datasets of retinal fluid
segmentation in 3D Optical Coherence Tomography (OCT), various network
configurations, and verification across 10 different network initializations.
In an unsupervised domain adaptation context, SegCLR achieves results on par
with a supervised upper-bound model trained on the intended target domain.
Notably, we discover that the segmentation performance of SegCLR framework is
marginally impacted by the abundance of unlabeled data from the target domain,
thereby we also propose an effective zero-shot domain adaptation extension of
SegCLR, eliminating the need for any target domain information. This shows
that our proposed addition of contrastive loss in standard supervised training
for segmentation leads to superior models, inherently more generalizable to
both in- and out-of-domain test data. We additionally propose a pragmatic
solution for SegCLR deployment in realistic scenarios with multiple domains
containing labeled data. Accordingly, our framework pushes the boundaries of
deep-learning based segmentation in multi-domain applications, regardless of
data availability —– labeled, unlabeled, or nonexistent.

**footnotetext: Corresponding author<EMAIL_ADDRESS>
##  I Introduction

Routine assessment of eye conditions in clinics is being transformed by the
applications of deep learning in ophthalmology. These methods facilitate
quantitative analysis of 3D images of eyes acquired with Optical Coherence
Tomography (OCT) devices [1], which create volumetric datasets by stacking 2D
slices known as B-scans. The use of supervised learning in segmentation of
retinal fluids, mainly via UNet [2] based neural networks, has led to advances
in diagnosis, prognosis, and a deeper understanding of eye diseases, such as
neovascular age-related macular degeneration (nAMD) and Diabetic Macular Edema
(DME) [3, 4, 5, 6]. However, training such supervised deep neural networks
requires large amounts of labeled data, the procurement of which is costly and
sometimes infeasible. Most importantly, such labeling needs to be repeated for
each problem setting/domain; since trained models often fail when inference
data differs from labeled examples, so-called _domain-shift_ , e.g., between
images from different eye diseases and different OCT devices [7].

Domain adaptation techniques aim to maintain a similar performance on images
from new domains while reducing the annotation effort required in such newer
datasets. Unsupervised domain adaptation aims to leverage information learned
from a labeled data domain for applications in other domains where only
unlabeled data is available. To this end, many deep learning methods have been
proposed [8], mostly using generative adversarial networks, e.g., to translate
visual appearance across OCT devices [9]. Ultimately, it is desirable for
learned models to implicitly generalize to other domains without requiring any
new data, i.e., ideally not even unlabeled data from a new domain. This
scenario, known as zero-shot domain adaptation [10], provides huge advantages
in real-world deployment by obviating any retraining or customization being
required per test domain encountered.

Advances in self-supervised learning have been increasingly successful in
extracting informative features from images without accessing their labels,
hence enabling the exploitation of much larger unlabeled datasets [11, 12, 13,
14, 15, 16, 17, 18]. These methods usually employ different versions of
Siamese networks [19] that can be applied to different inputs in parallel
while sharing their weights. For self-supervision, typically different
augmentations of an image are treated as positive samples, which are desired
to map close-by or to a single point on a learned manifold, and therefore
minimizing the feature distances between these positive samples when passed
through a Siamese network. To prevent such optimization from collapsing to a
constant output, different approaches have been proposed: For instance,
SimSiam [14] employs a stop-gradient operation for one of the Siamese
encoders. Another successful strategy, contrastive learning (CL), introduces
the concept of negative pairs, the distances of which should meanwhile be
maximized. SimCLR [12] is one of the most widely-adopted CL frameworks. Self-
supervised learning is commonly used for model pretraining, typically based on
natural images such as ImageNet [20]. Such pretrained model is subsequently
fine-tuned or distilled for downstream tasks such as classification,
detection, or segmentation [13].

Models pretrained with natural images are of limited use for medical
applications, which often involve images with volumetric content and with
substantially differing appearances from natural images. This has led to
recent application-specific approaches for CL pair generation in medical
context [21, 22]. USCL [22] minimizes the feature distance between frames of
the same ultrasound video, while maximizing the distance between frames of
different videos, in order to produce pretrained models for ultrasound
applications. USCL also proposes a joint semi-supervised approach, which
simultaneously minimizes a contrastive and supervised _classification_ loss.
However, to be applicable for image segmentation, this method relies on
subsequent fine-tuning, which is potentially sub-optimal for preserving the
unlabeled information for the intended task of segmentation. In fact, there
exist little work on CL methods on image segmentation without fine-tuning.

We propose SegCLR as a CL solution to improve segmentation quality by
leveraging both manual annotations and unlabeled data. To that end we
introduce a semi-supervised framework for joint training of CL together with
segmentation labels. Preliminary results of this work have been presented as a
conference contribution [23] for two datasets acquired with different OCT
scanners, where the domain shift originates from a change in acquisition
device. SegCLR without any target-domain labels is shown to achieve
unsupervised domain adaptation results close to an upper bound that uses
supervised learning on the target domain. Our additional contributions in this
work are: (1) In addition to acquisition device change, we present results
with a third dataset with a new disease indication, which helps demonstrate
the benefits of SegCLR also for domain shift in disease type. (2) An analysis
of the amount of unlabeled target data is presented, indicating its limited
effect on the resulting segmentation performance. (3) Exploiting the finding
above, we propose a zero-shot domain adaptation use-case of our SegCLR
framework, for which an extensive analysis reveals substantial benefits
showing that SegCLR trained models are implicitly better at generalizing to
unseen domains. (4) An analysis of the stability of the results upon distinct
network initialisations reveals that most proposed SegCLR variants perform
well and similarly, confirming the stability of the overall SegCLR framework
to method setting choices such as pair generation and contrastive projection
strategies. This further highlights the importance of analyzing many
randomized result replicates in assessing deep learning models in general. (5)
In a multi-domain training scenario of SegCLR, we show that with labeled data
available from multiple domains, SegCLR applied to the domains simultaneously
yields results superior to conventional supervised training as well as SegCLR
trained on individual domains, potentially better leveraging cross-domain
information.

##  II Methods

The considered learning frameworks are illustrated in Fig. 1. Let a _source_
domain ($D^{s}$) contain labeled data, with which _Baseline_ models can be
trained following typical _supervised_ learning schemes. Distinct domains for
which only unlabeled images can be observed are considered _target_ domains
($D^{t}$). We first study an unsupervised domain adaptation setting as in
[23], where SegCLR is trained for a source domain $D^{s}$ and a target domain
$D^{t}$ by using a combination of supervision and contrastive losses. We then
investigate its zero-shot domain adaptation application, where SegCLR is
trained using only data from a labeled source domain $D^{s}$, while being
applied on unseen target domains $D^{t}$, i.e., on domains for which no data
(labeled or unlabeled) is used in training.

Figure 1: Illustration of Unsupervised Domain Adaptation (UDA) and Zero-Shot
Domain Adaptation frameworks studied herein for SegCLR. The colored ellipses
indicate the losses to use in training, with the variable superscripts
representing the domain being source (s) or target (t).

###  II-A Supervised learning

For the segmentation backbone, we adopted the proven UNet architecture [2]
(details in Fig. 2), which can be modeled as $F(\cdot)$ processing an image
$x$ to predict a segmentation map $p=F(x)$ to approximate an (expert-
annotated) ground truth segmentation $y$. $F$ is learned by minimizing a
supervised loss $\mathcal{L}_{\mathrm{sup}}$, which in our work is the
logarithmic Dice loss of labeled data in a source domain $D^{s}$:

$\mathcal{L}_{\mathrm{sup}}=-\sum_{(p_{i},y_{i})\in
D^{s}}\log{\frac{2\sum_{j\in\mathrm{pixels}}y_{i}^{j}p_{i}^{j}}{\epsilon+\sum_{j\in\mathrm{pixels}}(y_{i}^{j}+p_{i}^{j})}}$
(1)

for all training images $(x_{i},y_{i})\in D^{s}$, where $\epsilon$ is a small
number to avoid division by 0.

Figure 2:  SegCLR architecture employed for joint supervised and contrastive
learning. Layers are represented as arrows and their outputs as rectangles.
The width and height of these outputs is annotated at the upper left of the
rectangles, and the number of features at the bottom. $F(\cdot)$ is the
segmentation backbone, $E(\cdot)$ the encoder, and $C(\cdot)$ the contrastive
projection. The architectures of $\rho^{\mathrm{agg}}$ and
$\rho^{\mathrm{MLP}}$ are described in Section II-E.

###  II-B Self-supervised learning

Self-supervised learning aims to optimize an encoder $E(\cdot)$ to achieve
good representations $h=E(x)$ without the need of manually annotated labels
$y$. In the literature (for mostly classification tasks), the ResNet
architecture is commonly used for $E(\cdot)$. Instead, we use the existing
UNet encoder in order to adapt the learned features $h$ for our intended
segmentation task. A subsequent contrastive projection head $C(\cdot)$ then
maps the bottleneck-layer features to vector projections $z=C(h)$ on which the
contrastive loss $\mathcal{L}_{\mathrm{con}}$ is applied. The architecture
used for $E(\cdot)$ and $C(\cdot)$ is illustrated in Fig. 2. We herein employ
two widely adopted self-supervised learning frameworks: SimCLR [12] and
SimSiam [14].

In SimCLR, $\mathcal{L}_{\mathrm{con}}$ aims to minimize the distance between
_positive_ pairs of images and maximize the distance between _negative_ pairs.
The positives $(x_{i}^{\prime},x_{i}^{\prime\prime})$ are created from each
image $x_{i}$ by a defined pair generator $P(\cdot)$ described further in
Section II-D below, i.e.
$P(x_{i})\rightarrow(x_{i}^{\prime},x_{i}^{\prime\prime})$. The negatives
pairs $(x_{i},x_{k})$ can be formed using other images $x_{k}$ for $k\neq i$.
We employ a version of the normalized temperature-scaled cross entropy loss
[18] adapted to our problem setting as:

$L_{\mathrm{con}}^{\mathrm{CLR}}=\sum_{P(x_{i}),\ x_{i}\in
D}\big{(}\,l(z_{i}^{\prime},z_{i}^{\prime\prime})+l(z_{i}^{\prime\prime},z_{i}^{\prime})\,\big{)}$
(2)
$l(z_{i}^{\prime},z_{i}^{\prime\prime})=-\log\frac{\exp{\big{(}d(z_{i}^{\prime},z_{i}^{\prime\prime})}/\tau\big{)}}{\sum_{x_{k}\in
D}\mathbbm{1}_{[k\neq i]}\exp{\big{(}d(z_{i}^{\prime},z_{k})/\tau\big{)}}}$
(3)

where $d(u,v)=(u\cdot v)/(||u||_{2}\,||v||_{2})$ and $\tau$ is the temperature
scaling parameter.

In SimSiam, a learnable predictor $Q(\cdot)$ is applied on the projection from
one network path to predict that from the other:

$L_{\mathrm{con}}^{\mathrm{Siam}}=-\sum_{x_{i}\in
D}\Big{(}d\big{(}Q(z_{i}^{\prime}),z_{i}^{\prime\prime}\big{)}+d\big{(}Q(z_{i}^{\prime\prime}),z_{i}^{\prime}\big{)}\Big{)}$
(4)

where the gradients from the second path are omitted for network weight
updates (_stopgrad_) in back-propagation.

###  II-C Joint semi-supervised and contrastive learning

We adapt the USCL joint training strategy, which was proposed for US video
classification, to our segmentation task on 3D images by combining
$\mathcal{L}_{\mathrm{sup}}$ and $\mathcal{L}_{\mathrm{con}}$ in a semi-
supervised framework illustrated in Fig. 3. Considering a source domain
$D^{\mathrm{s}}$ and a target domain $D^{\mathrm{t}}$, total loss
$\mathcal{L}$ is calculated as follows:

$\mathcal{L}=\frac{1}{2}\left(\underset{x\in
D^{\mathrm{s}}}{\mathcal{L}_{\mathrm{con}}}+\underset{x\in
D^{\mathrm{t}}}{\mathcal{L}_{\mathrm{con}}}\right)+\lambda\underset{(x,y)\in
D^{\mathrm{s}}}{\mathcal{L}_{\mathrm{sup}}}$ (5)

where $\lambda$ is a hyperparameter that controls relative contributions.

Figure 3:  Illustration of semi-supervised contrastive learning framework for
unsupervised domain adaptation. The SegCLR block corresponds to the
architecture in Fig. 2. The _repel_ losses are not used by SimSiam. Supervised
losses are only used when labeled images exist. While this framework is
flexible to accommodate any number of labeled images, at least one is required
to drive the decoder arm of the underlying UNet.

###  II-D Pair generation strategies

The approach chosen for contrastive pair generation is key in self-supervised
learning. We herein propose and compare different pair generation functions
$P(\cdot)$ for volumetric OCT images, as illustrated in Fig. 4.

First, the augmentation-based pair formation typically employed for natural
images (e.g., in SimCLR and SimSiam) is adapted for volumetric OCT images,
denoted as $P_{\mathrm{a}}$. To that end, labeled slices in $D^{\mathrm{s}}$
and random slices in $D^{\mathrm{t}}$ are augmented with horizontal flipping
($p=0.5$), horizontal and vertical translation (within 25% of the image size),
zoom in (up to 50%), and color distortion (brightness up to 60% and jittering
up to 20%). For color augmentation, images are first transformed to RGB and
then back to grayscale.

Alternatively, we propose a slice-based pairing $P_{\mathrm{s}}$ that
leverages the expected coherence of nearby slices in a 3D volume for CL. For
this, $x_{i}^{\prime}=x_{i}$ is chosen with a slice index $b_{i}^{\prime}$ in
3D. Then, $x_{i}^{\prime\prime}$ is a slice from the same volume with the
(rounded) slice index $b_{i}^{\prime\prime}$ sampled from a Gaussian
distribution $\phi$ centered on the index of the original image, i.e.,
$b_{i}^{\prime\prime}\sim\phi(b_{i}^{\prime},\sigma)$, with standard deviation
$\sigma$ as a hyperparameter. Combining the two pairing strategies yields
$P_{\mathrm{s+a}}$ where $P_{\mathrm{s}}$ is used first and the augmentations
in $P_{\mathrm{a}}$ are then applied on the selected slices.

Figure 4: Proposed pair generation approaches for contrastive learning with 3D
images using SegCLR.

###  II-E Aggregation of contrastive features

A projection head $C(\cdot)$ is formed by an aggregation function
$\rho^{\mathrm{agg}}$ that aggregates features $h$ in a vector, which is then
processed by a multilayer perceptron $\rho^{\mathrm{MLP}}$ to create
projection $z$. Typical contrastive learning frameworks such as SimCLR and
SimSiam use a projection (denoted herein by $C_{\mathrm{pool}}$) where
$\rho_{\mathrm{pool}}^{\mathrm{agg}}:\mathbb{R}^{w\times h\times
c}\rightarrow\mathbb{R}^{1\times 1\times c}$ is a global pooling operation on
the width $w$, height $h$, and channels $c$ of the input features. Such
projection $C_{\mathrm{pool}}$ may be suboptimal for learning representations
to effectively leverage segmentation information, as backpropagation from
$\mathcal{L}_{\mathrm{con}}$ would lose the spatial context. Instead we
propose $C_{\mathrm{ch}}$, for which
$\rho_{\mathrm{ch}}^{\mathrm{agg}}:\mathbb{R}^{w\times h\times
c}\rightarrow\mathbb{R}^{w\times h\times 1}$ is a $1$$\times$$1$$\times$$1$
convolutional layer that learns layer aggregation while preserving spatial
context.

###  II-F Implementation

Adam optimizer [24] was used in all models, with a learning rate of $10^{-3}$.
Dropout with $p=0.5$ is applied on the layers illustrated with orange arrows
in Fig. 2. The projection head $\rho^{\textrm{MLP}}$ in $C(\cdot)$ is formed
by two fully-connected layer with 128 units each, where the first one uses
group normalization [25] (with a group size of 4) and ReLU activation. We
heuristically set $\lambda=20$ and the standard deviation for $P_{\mathrm{s}}$
as $\sigma=250\,\mu m$, which is the range for which we observe roughly
similar features across slices. Supervised models were trained for 200 epochs,
and the model at the epoch with the highest average Dice coefficient across
classes on the validation set was selected for evaluation on a holdout test
set. Our implementation is in Tensorflow 2.7, ran on an NVIDIA V100 GPU.

###  II-G Evaluation setup

Individual slices of the 3D OCT volumes are segmented with UNet in 2D. All
reported experiments were replicated by training from scratch with 10
different initialization seeds, to factor out the sensitivity to randomness in
network initialization. Model performance was then also evaluated slice-wise
using slices with ground-truth annotations, based on the Dice coefficient as
an overlap metric and Unnormalized Volume Dissimilarity (UVD) as an error
metric for the total segmentation error (FP+FN). Compared to Dice, UVD is more
robust to FP on B-scans with small annotated regions for individual classes.
Dice is reported as % (higher is better) and UVD as $\mu m^{3}$x$10^{2}$
(lower is better).

Averaging metrics across classes with a large variation may lead to bias.
Thus, for the metrics reported as boxplots, each per-slice metric $m^{c}_{i}$
for method $i$ and class $c$ is first normalized by its class Baseline
$m^{c}_{\textrm{bas}}$, and then these are averaged over all $c$ and images on
the test set. The average of all Baseline replicates is used as
$m^{c}_{\textrm{bas}}$ for calculating the _relative metrics_ of all other
models. Absolute metrics for each class are also reported in the tables as
mean $\pm$ standard deviation. To facilitate model comparison, the metrics
included in most tables in the main text are averaged across classes. To
further allow for the interpretation and comparison of absolute segmentation
performances, we included detailed tables in Supplementary Materials with
metrics separately for individual labels.

For comparing and ranking models, we adapted the strategy from [26] to
simultaneously consider both Dice and UVD as complementary metrics.
Accordingly, the models are first ranked among each other on individual
B-scans, separately based on their Dice and UVD. These rankings are next
averaged across the B-scans and the two metrics, to obtain and report a final
ranking of each model for each domain.

##  III Dataset

We employ three large OCT datasets from clinical trials. As each dataset
involve different eye diseases and/or acquisition devices, each then denotes a
distinct domain $D_{i},\,i\in\\{1..3\\}$, with details as follows:

$\bullet$ $D_{1}$: Images of neovascular age-related macular degeneration
(nAMD) patients, acquired using a _Spectralis_ (Heidelberg Engineering)
imaging device, yielding scans of $512\times 496\times 49$ or $768\times
496\times 19$ voxels, with a resolution of $10\times 4\times 111$ or $5\times
4\times 221$ $\mu$m/voxel, respectively. These were acquired as part of the
phase-2 AVENUE trial (NCT02484690).

$\bullet$ $D_{2}$: Images of nAMD patients, acquired using a _Cirrus_ HD-OCT
III (Carl Zeiss Meditec) imaging device, yielding scans with $512\times
1024\times 128$ voxels and a resolution of $11.7\times 2.0\times 47.2$
$\mu$m/voxel. These were acquired as part of the phase-3 HARBOR trial
(NCT00891735).

$\bullet$ $D_{3}$: Images of diabetic macular edema (DME) patients, acquired
using a _Spectralis_ device with scan sizes and resolutions the same as for
$D_{1}$. These were acquired as part of the phase-2 BOULEVARD trial
(NCT02699450).

All slices (B-scans) from the three datasets were resampled to $512\times 512$
pixels with the approximate resolution of $10\times 4$ $\mu$m/pixel. Selected
B-scans from $D_{1}$ and $D_{2}$ were manually annotated for fluid regions of
potential diagnostic value, i.e., intraretinal fluid (IRF), subretinal fluid
(SRF), pigment epithelial detachment (PED), and subretinal hyperreflective
material (SHRM). B-scans from $D_{3}$ were annotated for IRF and SRF, but not
PED nor SHRM, as these are not relevant for diabetic macular edema patients.
More details on these datasets and the annotation protocol can be found in
[27].

Labeled data exists for all 3 domains as detailed in Table S1. For each
experimental setting, different ablation combinations were performed by
training models on labeled source domain(s) $D^{s}$ and applying the trained
models on unlabeled domain(s) $D^{t}$. When a domain is considered $D^{t}$, we
omit its labels in the training and use them only for ($i$) evaluating the
models on the test set of that domain, or ($ii$) training of an _UpperBound_
model used as a reference. Our data stratification is detailed in Table S1.

##  IV Results and Discussion

In most experimental settings, we train a model $F$ concurrently on $(x,y)\in
D^{\mathrm{s}}$ and $x\in D^{\mathrm{t}}$, and then apply this trained model
on the target data for evaluating
$F(x\,|\,x\\!\in\\!D^{\mathrm{t}})\rightarrow y\in D^{\mathrm{t}}$. In the
zero-shot DA setting, we train only on $(x,y)\in D^{\mathrm{s}}$, without any
information from $D^{\mathrm{t}}$. We evaluate the trained models also on
their initial source domain $F(x\,|\,x\\!\in\\!D^{\mathrm{s}})\rightarrow y\in
D^{\mathrm{s}}$ to assess the retention of source-domain segmentation
capability.

###  IV-A Joint learning for unsupervised domain adaptation across imaging
devices

We first evaluate the proposed SegCLR framework in an unsupervised domain
adaptation setting where the domain shift is caused by images being acquired
with different imaging devices. To address the DA between Spectralis and
Cirrus devices, the source and target domains were chosen as $D^{s}=D_{1}$ and
$D^{t}=D_{2}$, since the latter domain has many more unlabeled images to
facilitate unsupervised DA. This also helps us replicate the setting in [23]
showing our preliminary results.

TABLE I:  Absolute segmentation metrics averaged across classes in the
unsupervised domain adaptation setting using $D^{s}=D_{1}$ and $D^{t}=D_{2}$.
Results with the best (underlined) and the second best performance are in bold
(excluding UpperBound).

Model | Dice | UVD
---|---|---
2-9 | $D^{t}$ | $D^{s}$ | $D^{t}$ | $D^{s}$
UpperBound | 61.30$\pm$ | 4.34 | N/A |  | 9.61$\pm$ | 1.42 | N/A |
Baseline | 39.23$\pm$ | 8.18 | 67.14$\pm$ | 8.24 | 16.47$\pm$ | 2.15 | 6.00$\pm$ | 2.32
SimCLR | 41.01$\pm$ | 7.32 | 66.70$\pm$ | 8.09 | 16.14$\pm$ | 1.89 | 6.08$\pm$ | 2.40
SimSiam | 41.14$\pm$ | 7.86 | 67.40$\pm$ | 8.63 | 15.98$\pm$ | 1.89 | 6.03$\pm$ | 2.44
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{pool}}$) | 58.09$\pm$ | 5.85 | 67.24$\pm$ | 6.95 | 11.77$\pm$ | 1.80 |  5.98$\pm$ | 2.37
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) | 39.39$\pm$ | 8.38 | 52.24$\pm$ | 7.35 | 17.25$\pm$ | 6.00 | 8.29$\pm$ | 3.27
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{pool}}$) |  59.62$\pm$ | 5.52 | 66.89$\pm$ | 7.32 | 11.63$\pm$ | 2.16 | 5.97$\pm$ | 2.27
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) |  58.32$\pm$ | 5.34 |  67.93$\pm$ | 6.91 |  11.62$\pm$ | 2.40 |  5.85$\pm$ | 2.27
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{ch}}$) | 50.60$\pm$ | 8.98 | 66.38$\pm$ | 7.06 | 13.25$\pm$ | 3.70 | 6.13$\pm$ | 2.41
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) | 58.09$\pm$ | 5.71 |  67.29$\pm$ | 6.64 |  11.56$\pm$ | 2.03 | 6.07$\pm$ | 2.39

A supervised UNet model, _Baseline_ , is trained only on the source domain
$D^{\mathrm{s}}$. An _UpperBound_ supervised model trained on labeled data
from $D^{t}$ is included for comparison. This labeled data is used here only
as a reference and is ablated for all other models. Table I shows very poor
results for Baseline on $D^{t}$, while results are much better for UpperBound
on $D^{t}$ or Baseline on $D^{s}$, confirming that the two domains indeed
differ from supervised learning perspective. For easier comparison, Figure 5
shows the metrics relative to the average of Baseline models and averaged
across labels, i.e., so-called relative metrics. The distribution across 10
training repetitions are shown to highlight the large inherent variation in
these models, including Baseline.

Learning representations of $D^{\mathrm{t}}$ with SimCLR and SimSiam with
subsequent finetuning on $D^{\mathrm{s}}$ shows a slight improvement over
Baseline for $D^{t}$, as seen in Figure 5 by their ($\approx$2%) higher Dice
and lower UVD. This confirms that these pretraining strategies apply also on
our OCT data. The results for $D^{s}$ are comparable to Baseline, as the
finetuning on this domain, i.e., initializing the Baseline with the target-
learned representations, does not seem to add more information than existing
from the source supervision.

Figure 5:  Relative segmentation metrics for models using $D^{s}=D_{1}$ and
${D^{t}=D_{2}}$. The dashed line depicts the average Baseline result used as
reference for the relative metrics.

Our proposed _SegCLR_ framework is used here for joint training, which
augments SimCLR with a supervised loss and for contrasting segmentations
(Section II-C). SegCLR increases the number of network parameters only for
training and merely by 6.85% with respect to Baseline (UNet). The results in
Figure 5 show that most choices of the projection head $P$ and the contrastive
head $C$ lead to much better results (higher Dice and lower UVD) for SegCLR,
notably even surpassing the UpperBound in some cases. The only configurations
for which the results do not follow this positive trend involve the slice-
based pairing strategy $P_{s}$ alone, indicating that merely contrasting
nearby slices does not facilitate extracting features useful for segmentation.
While $P_{s}$ has the potential to be a complementary augmentation to $P_{a}$,
it does not seem to be useful for learning informative features by itself.
This corroborates other works on augmentations [11] reporting cropping to be a
crucial augmentation, which in our work is excluded when $P_{a}$ is not
applied. Indeed, most SegCLR configurations yield satisfactory results, with
only SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) failing substantially
compared to Baseline, both for $D^{t}$ and $D^{s}$. This is likely because the
pooling operation removes the spatial context between nearby and hence
relatively similar slices, hence preventing the learning of relevant features
from spatial correspondence. In comparison,
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{ch}}$) can easily capture pixel-wise
differences between nearby slices, thanks to $C_{\mathrm{ch}}$ being spatially
resolved, so that relevant features can be learned even with the
$P_{\mathrm{s}}$ pairing.

In [23] with a similar experimental setting we reported
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) as the superior SegCLR
configuration. Our strategy herein with repetition of experiments with
different initializations (10 random seeds) reveals a large dependence on
initializations, where sometimes different conclusions may be reached for
specific seeds. While such repeated evaluation strategy is computationally
expensive, we hope this observation to serve as an incentive to standardize
evaluation processes for generalizable conclusions.

Most configurations of SegCLR lead to results superior to other methods. In
Table I, SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) is seen to consistently
produce the best or the second best segmentation results for both $D^{s}$ and
$D^{t}$ for DA across imaging devices. In particular, this configuration is
seen to preserve the source domain information most successfully, with the
best metric in $D^{s}$ — notably even compared to the supervised Baseline.
This shows that supervised information from the labeled domain is not
forgotten (e.g., as a model capacity trade-off when learning from the large
unlabeled domain) and is indeed even enhanced and enriched with the added
contrastive strategy. Hence, SegCLR can be used for segmentation of both
$D^{s}$ and $D^{t}$, replacing the conventional Baseline approaches for the
labeled domains, while approaching UpperBound such that it obviates labeling
efforts for scenarios with no training data is or can be available in a target
domain.

###  IV-B Unsupervised domain adaptation across eye diseases

This section evaluates SegCLR for domain adaptation across eye diseases, i.e.,
when image appearances are similar but the content differs. To that end, an
experimental setting with $D^{s}=D_{1}$ and $D^{t}=D_{3}$ is used. Similarly
to the previous section, Figure 6 and Table II show that the Baseline model
trained on $D^{s}$ performs substantially worse on $D^{t}$ compared to an
UpperBound model trained on that target domain. Interestingly, in contrast to
the previous section, SimCLR, i.e., a model with contrastively pretrained
features, is worse than the Baseline for both $D^{s}$ and $D^{t}$ in this
setting. This can be due to instabilities in the contrastive loss, which do
not affect SegCLR thanks to the proposed joint training strategy.

TABLE II:  Absolute segmentation metrics averaged across classes in the
unsupervised domain adaptation setting using $D^{s}=D_{1}$ and $D^{t}=D_{3}$.
Results with the best (underlined) and the second best performance are in bold
(excluding UpperBound).

Model | Dice | UVD
---|---|---
2-9 | $D^{t}$ | $D^{s}$ | $D^{t}$ | $D^{s}$
UpperBound | 79.32$\pm$ | 14.25 | N/A |  | 6.57$\pm$ | 4.91 | N/A |
Baseline | 64.77$\pm$ | 11.71 | 67.14$\pm$ | 8.24 | 9.38$\pm$ | 7.06 | 6.00$\pm$ | 2.32
SimCLR | 55.33$\pm$ | 5.83 | 63.67$\pm$ | 6.53 | 10.13$\pm$ | 6.70 | 6.32$\pm$ | 2.30
SimSiam | 65.71$\pm$ | 12.23 | 66.90$\pm$ | 7.81 | 9.21$\pm$ | 6.87 | 5.95$\pm$ | 2.33
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{pool}}$) | 72.49$\pm$ | 15.29 | 68.48$\pm$ | 7.36 | 7.94$\pm$ | 5.96 | 5.86$\pm$ | 2.31
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) | 63.13$\pm$ | 15.61 | 63.01$\pm$ | 7.41 | 10.49$\pm$ | 7.08 | 6.25$\pm$ | 2.40
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{pool}}$) | 71.48$\pm$ | 15.94 |  68.64$\pm$ | 6.94 | 8.46$\pm$ | 5.87 |  5.83$\pm$ | 2.33
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) |  73.52$\pm$ | 16.07 | 68.25$\pm$ | 7.01 |  7.85$\pm$ | 5.93 | 5.93$\pm$ | 2.41
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{ch}}$) | 65.00$\pm$ | 11.09 | 65.85$\pm$ | 7.10 | 9.82$\pm$ | 6.68 | 6.20$\pm$ | 2.50
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) |  73.99$\pm$ | 16.40 |  68.67$\pm$ | 7.45 |  7.56$\pm$ | 5.75 |  5.84$\pm$ | 2.25

Figure 6:  Relative segmentation metrics for models using $D^{s}=D_{1}$ and
${D^{t}=D_{3}}$. The dashed line depicts the Baseline results used as
reference for the relative metrics.

Similarly to the DA setting in the previous subsection, Figure 6 confirms that
SegCLR with most of its configurations substantially outperforms Baseline for
$D^{t}$, while also yielding marginally better results for $D^{s}$. Only the
configurations with $P_{\mathrm{s}}$ alone leads to results inferior to
Baseline, with poorest results with the
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) configuration. In particular,
Table II shows SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) overall performs
the best, with SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) being a close
follower. The proposed method SegCLR being successful also in this different
DA setting between eye diseases also involving different label sets reinforces
the conclusions for its proposed use in diverse DA settings. Indeed, its
superior performance also in the source domain indicates its utility as a
general learning framework for either labeled or unlabeled data from multiple
domains is concerned.

###  IV-C Selecting the reference SegCLR implementation

To select a specific SegCLR configuration for subsequent experiments, we
tabulate average rankings of the models in Table III that summarize the
results of both studied DA settings from the previous two subsections.

TABLE III:  Ranking (lower is better) of method variants for the experiments
of domain adaptation across devices in Section IV-A ($D^{t}=D_{2}$) and across
diseases in Section IV-B ($D^{t}=D_{3}$) following the reporting scheme
described in Section II-G. The results are sorted by Overall ranking, which is
the average ranking from both experiments across $D^{s}$ and $D^{t}$.

Model | $D^{t}=D_{2}$ | $D^{t}=D_{3}$ | Overall
---|---|---|---
2-5 | $D^{t}$ | $D^{s}$ | $D^{t}$ | $D^{s}$
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) | 3.65 | 4.66 | 3.68 | 4.75 | 4.21
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) | 3.61 | 4.88 | 3.67 | 4.67 | 4.24
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{pool}}$) | 3.82 | 4.72 | 3.98 | 4.73 | 4.34
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{pool}}$) | 3.52 | 4.81 | 4.24 | 4.63 | 4.38
SimSiam | 6.17 | 4.37 | 4.89 | 4.71 | 4.91
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{ch}}$) | 4.81 | 4.75 | 5.02 | 5.21 | 4.96
Baseline | 6.51 | 4.47 | 5.11 | 4.68 | 5.05
SimCLR | 6.21 | 4.64 | 5.95 | 5.71 | 5.59
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) | 6.59 | 7.54 | 5.85 | 5.72 | 6.38

The rankings confirm the clear advantage of most SegCLR models relative to
Baseline, SimCLR, and SimSiam. They also show that pretrained models do not
help in such unspervised domain adaptation tasks, for which joint training
with SegCLR appears to be the key (with the only exception for the use of the
pairing strategy $P_{\mathrm{s}}$, as discussed above). Although the rankings
differ slightly depending on whether the source or the target domain
performance is prioritized, overall SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$)
is seen to be the best performer. By not requiring slice sampling, this model
also has a simpler implementation in practice, e.g., compared to
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$), which is a top contender. In
addition, the proposed $C_{\mathrm{ch}}$ adds a mere 0.03% more parameters to
the baseline model. Accordingly, we chose
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) as the reference configuration for
our proposed method, calling it simply SegCLR henceforth, to use in the
subsequent experiments and ablations.

###  IV-D Effect of amount of target data on SegCLR

The success of modern self-supervised learning frameworks has been largely
attributed to the availability and access to large unlabeled datasets [12, 15,
14, 13]. Previous sections demonstrate the efficacy of SegCLR given such large
datasets, from extensive clinical trials which are rarely available in
practice. An essential question thus remains as to how such framework performs
in scenarios with smaller sets of unlabeled data. This question is studied
here as an ablation study by reducing the amount of unlabeled data. We
evaluate this with the experimental setting for DA across imaging devices
(Section IV-A).

Figure 7 presents the results of SegCLR when trained with logarithmically
reducing amount of unlabeled data compared to the earlier experiments. The
experiments are taken down to the point of including no unlabaled data, i.e.,
the contrastive loss being computed only using the source domain data (jointly
with its supervised loss). Results show that the source domain performance is
not affected much by the amount of unlabeled data from a different domain,
which is expected since the supervised source domain is the major source of
information and the main driver for this task. Interestingly, however,
regardless of the amount of unlabaled data, SegCLR performs roughly 1% in Dice
on average compared to Baseline. This suggests that the proposed framework and
contrastive strategy implicitly advances and improves standard UNet training
and its generalization to test data from the same source domain, regardless of
data existence from yet another domain.

Results in Figure 7 for the target domain of interest shows an expected trend
of deterioration with diminishing amount of target data. Nevertheless,
surprisingly the reduction in performance is quite minor, and a reduction of
only 5.25$\pm$7.33% Dice and -0.99$\pm$2.13% UVD is observed between no
unlabeled target data and 100% unlabeled target data (of nearly 7 million
B-scans). Strikingly, SegCLR without any unlabeled data (i.e., SegCLR zero-
shot learning) performs still significantly superior to Baseline, by
13.84$\pm$6.22% Dice and -3.85$\pm$1.64% UVD. This again suggests that the
introduced framework with the joint learning strategy inherently enables
better generalization, even in supervised settings without requiring
additional data and without adding much to the network complexity. We further
study this surprising observation in the next section.

Figure 7:  Segmentation results of SegCLR for ablation experiment of unlabeled
data size. The 95% confidence interval is shown as blue bands for SegCLR and
as gray dotted lines for Baseline replicates. The average of Baseline
replicates (black dashed line at 0) is the basis for relative comparisons,
i.e., a rel. Dice above 0 or a rel. UVD below 0 indicates a superior
performance by SegCLR.

###  IV-E Zero-shot domain adaptation on different domain combinations

In this section, we assess SegCLR zero-shot DA capability across different
experimental settings. To that end, we consider all possible combinations of
the datasets $D_{1}$, $D_{2}$, and $D_{3}$ as $D^{s}$ and $D^{t}$. Note that
in this zero-shot setting, $D^{t}$ is exclusively reserved for evaluation,
with its data (including unlabeled images) not been seen during the training
phase.

Figure 8 and Table IV demonstrate the superiority of SegCLR over Baseline
(UNet) consistently in nearly all experimental dataset combinations. The only
setting SegCLR is clearly inferior (for both Dice and UVD) is for training on
$D^{s}=D_{3}$ and evaluating on $D^{t}=D_{1}$, for which the imaging device
stays the same but the disease type (labeling info and label sets) mainly
differ. Note that with the imaging device DA from $D^{s}\in\\{D_{1},D_{2}\\}$
to $D^{t}=D_{2}$ leads a relatively larger reduction in performance for
Baseline, which cannot generalize across such large domain gap — a setting in
which SegCLR appears to be relatively successful. Visual examples from
different experimental combinations are shown in Figure 9.

Figure 8:  Results for zero-shot domain adaptation models trained on different
source domains $D^{s}$ shown in the x axes, with $D^{\mathrm{All}}=D_{1}\cup
D_{2}\cup D_{3}$. Each graph shows the evaluation results on each target
domains $D^{t}$. The metrics are relative to the average of the Baseline
models trained on the same $D^{s}$. The distributions of Baselines are also
provided to illustrate the variation in Baseline repetitions. A positive Dice
value for training on $D_{i}$ and evaluating on $D_{j}$ means that a zero-shot
CL with SegCLR from domain $i$ applied to domain $j$ is superior to
conventional supervised-only training. This indicates that the domain gap
between $i$ and $j$ can be bridged by representation learning with SegCLR.
Note that, although not directly observable in these normalized plots, the
absolute Baseline performance for $i=j$ is much higher than otherwise, as
expected. These results for $i=j$ show that SegCLR is superior to traditional
supervised learning Baseline, even when applied on the trained domain itself,
i.e., without any domain shift. SegCLR can thus augment and replace
conventional UNet for better generalizability.

Overall, SegCLR is seen to outperform Baseline across different combinations
of source and target datasets. Additionally, SegCLR surpasses Baseline even
when evaluated within the same source domain. This suggests that the benefits
of SegCLR extend beyond DA alone, proving valuable even for conventional
supervised settings with training and deployment within the same domain, i.e.,
as a plug-and-play replacement for UNet.

Collectively, these results affirm the prior hypothesis that SegCLR exhibits
robust performance in realistic scenarios where deployment on entirely unseen
domains is anticipated, particularly when the domain gap arises from changes
image apperance, e.g., across different imaging devices.

Figure 9: Qualitative assessment of SegCLR and Baseline, trained on different
domains (rows) for example images from each domain (columns). Note that
$D_{3}$ only has two of the four labels. These visually examplify that ($i$)
training and evaluating on the same domain works well with both Baseline and
SegCLR, ($ii$) SegCLR outperforms Baseline when training and evaluation
domains differ, i.e., in UDA settings, ($iii$) multi-domain training
$D_{\mathrm{All}}$ performs better than single-domain training, for both
Baseline and SegCLR, with minor superiority of SegCLR (see bottom-right for
$D_{3}$).

###  IV-F Multi-domain supervised contrastive learning

Although the evaluations so far above considered only single source domains,
it is common in real-world contexts to have data available from multiple
domains. For conventional supervised training, preliminary findings, e.g.,
[28], suggest that training with multi-domains simultaneously may yield
superior segmentation outcomes on each domain separately, compared to models
trained individually on each domain. This indicates that different domains may
bring in complementary and supporting information in a multi-domain training
setting. We test here SegCLR in such practical setting of multi-domain
training.

We train both Baseline and SegCLR models on $D^{s}=D_{\mathrm{All}}\equiv
D_{1}\cup D_{2}\cup D_{3}$, evaluating the two trained models on each domain
separately. Note that no unlabeled data is used in this setting, where only
the images from the annotated training data were used for contrastive
learning. Quantitative results are shown in Figure 8 and Table IV, with
examples depicted in Figure 9. The results corroborate the findings in [28] in
that Baseline trained on $D_{\mathrm{All}}$ outperforms all other individual
Baseline models for the corresponding target domains (i.e., , comparing the
rows of Baseline across each column), except for a minuscule margin in Dice
for $D_{3}$. The observation further extends to SegCLR, i.e., when trained on
$D_{\mathrm{All}}$, SegCLR consistently outperforms Baseline as well as all
other individual-domain-trained SegCLR results, for all examined domains and
both metrics. Evidently, SegCLR framework can effectively leverage contrastive
information even from labeled data, thereby enhancing model generalizability
across datasets as well as in-domain image variations. Consequently, SegCLR
emerges as an ideal framework also for supervised learning only with labeled
data, i.e., when there is no domain shift. This further includes settings
involving multiple domains for training and/or inference.

TABLE IV:  Zero-shot DA results for models trained on different $D^{s}$ (rows)
and evaluated on different $D^{t}$ (columns). Absolute metrics are shown
averaged across all classes, in bold for the best performance.

$D^{s}$ | Model | Dice | UVD
---|---|---|---
3-14 | $D_{\textrm{1}}$ | $D_{\textrm{2}}$ | $D_{\textrm{3}}$ | $D_{\textrm{1}}$ | $D_{\textrm{2}}$ | $D_{\textrm{3}}$
$D_{\textrm{1}}$ | Baseline | 67.14$\pm$ | 8.24 | 39.23$\pm$ | 8.18 | 64.77$\pm$ | 11.71 | 6.00$\pm$ | 2.32 | 16.47$\pm$ | 2.15 | 9.38$\pm$ | 7.06
SegCLR | 69.13$\pm$ | 7.94 | 53.07$\pm$ | 8.35 | 71.13$\pm$ | 17.16 | 5.94$\pm$ | 2.45 | 12.61$\pm$ | 2.24 | 8.27$\pm$ | 6.10
$D_{\textrm{2}}$ | Baseline | 46.01$\pm$ | 6.07 | 61.30$\pm$ | 4.34 | 40.72$\pm$ | 16.08 | 10.83$\pm$ | 4.80 | 9.61$\pm$ | 1.42 | 14.45$\pm$ | 7.97
SegCLR | 54.55$\pm$ | 6.27 | 64.08$\pm$ | 6.23 | 55.11$\pm$ | 22.72 | 8.23$\pm$ | 3.60 | 9.12$\pm$ | 1.58 | 11.20$\pm$ | 8.26
$D_{\textrm{3}}$ | Baseline | 43.58$\pm$ | 4.13 | 35.26$\pm$ | 7.58 | 79.45$\pm$ | 13.67 | 9.71$\pm$ | 2.79 | 22.94$\pm$ | 3.11 | 6.52$\pm$ | 4.79
SegCLR | 40.10$\pm$ | 5.25 | 40.50$\pm$ | 9.41 | 80.21$\pm$ | 13.02 | 11.00$\pm$ | 3.53 | 25.12$\pm$ | 10.40 | 6.06$\pm$ | 4.69
$D_{\textrm{All}}$ | Baseline | 67.72$\pm$ | 7.42 | 66.25$\pm$ | 3.82 | 79.40$\pm$ | 13.25 | 5.80$\pm$ | 2.17 | 8.26$\pm$ | 0.92 | 6.36$\pm$ | 5.00
SegCLR |  69.86$\pm$ |  7.25 |  69.48$\pm$ |  4.73 |  81.39$\pm$ |  13.18 |  5.76$\pm$ |  2.39 |  8.18$\pm$ |  1.15 |  5.78$\pm$ |  4.66

##  V Conclusion

We have introduced SegCLR, a novel framework for learning across different
domains, either labeled or unlabeled. Through a series of extensive
experiments on OCT datasets from different clinical trials, including
different acquisition devices and disease conditions, SegCLR with joint
training is demonstrated to be superior to conventional training and
contrastively-pretrained strategies. SegCLR can address significant domain
shifts in unsupervised domain adaptation (UDA), as demonstrated in scenarios
with changing imaging devices and eye diseases, with promising results for
both source and target domains. Our experimental observations highlight the
importance of training replicates in deep learning experimental work; a
procedure that, while obvious, is rarely used. While the proposed
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) outperforms other configurations
across all the experiments, most configurations leading to similar
improvements in results indicates the robustness of the proposed framework to
structural changes. Note that hyper-parameters were mostly similar across the
diverse experimental settings, reaffirming the robustness.

The effectiveness of SegCLR does not rely on the availability of large amounts
of unlabeled data, and it performs strikingly well even with limited or no
unlabeled data from the target domain, i.e., in unsupervised and zero-shot
domain adaptation scenarios. These have been demonstrated in a variety of
experimental settings, with the exciting implications as follows: First, on
the contrary to what can be anticipated, very large unlabeled datasets are not
required to benefit from SegCLR, and even minimal target data is helpful.
Second, impressive results can already be attained with no access to or
knowledge of the target domain at all! In such zero-shot domain adaptation
setting, labeled data is available only from source domain(s), with additional
target domains only encountered upon deployment. The versatility of SegCLR is
further demonstrated in a multi-domain training experiment where it is
concurrently trained on labeled data from multiple domains. The results affirm
that SegCLR is not only beneficial for new, unseen domains in a UDA setting,
but it also yields superior results in typical supervised settings with
labeled data, from both a single or multiple domains. Accordingly, we propose
SegCLR to augment conventional supervised UNet, with added generalizability
and without compromise.

In summary, our findings indicate that SegCLR provides a powerful and
versatile approach to tackle training and inference across multiple domains in
medical imaging. With its capability of learning from both labeled and
unlabeled data across diverse domains, SegCLR shows great promise for
improving the applicability of deep learning models to complex clinical
settings involving heterogeneous domains.

## References

  * [1] J. Fujimoto and E. Swanson, “The development, commercialization, and impact of optical coherence tomography,” _Investigative Ophthalmology & Visual Sci_, vol. 57, no. 9, 2016.
  * [2] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in _Int Conf on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2015, pp. 234–241.
  * [3] U. Schmidt-Erfurth and S. M. Waldstein, “A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration,” _Progress in Retinal and Eye Research_ , vol. 50, pp. 1–24, 2016.
  * [4] J. N. Sahni, A. Maunz, F. Arcadu, Y.-P. Zhang_Schaerer, Y. Li, T. Albrecht, A. Thalhammer, and F. Benmansour, “A machine learning approach to predict response to anti-vegf treatment in patients with neovascular age-related macular degeneration using sd-oct,” _Investigative Ophthalmology & Visual Science_, vol. 60, no. 11, pp. PB094–PB094, 2019.
  * [5] J. D. Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev _et al._ , “Clinically applicable deep learning for diagnosis and referral in retinal disease,” _Nature Medicine_ , vol. 24, no. 9, pp. 1342–1350, 2018.
  * [6] H. Bogunovic, F. Venhuizen, S. Klimscha, S. Apostolopoulos, A. Bab-Hadiashar _et al._ , “RETOUCH: The retinal OCT fluid detection and segmentation benchmark and challenge,” _IEEE Transactions on Medical Imaging_ , vol. 38, no. 8, pp. 1858–1874, 2019.
  * [7] T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” _Ophthalmology_ , vol. 125, no. 4, pp. 549–558, 2018.
  * [8] M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” _Neurocomputing_ , vol. 312, pp. 135–153, 2018.
  * [9] M. Ren, N. Dey, J. Fishbaugh, and G. Gerig, “Segmentation-Renormalized deep feature modulation for unpaired image harmonization,” _IEEE Transactions on Medical Imaging_ , vol. 40, no. 6, pp. 1519–1530, 2021.
  * [10] K.-C. Peng, Z. Wu, and J. Ernst, “Zero-shot deep domain adaptation,” in _Proceedings of the European Conference on Computer Vision (ECCV)_ , 2018, pp. 764–781.
  * [11] M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in _IEEE Int Conf on Computer Vision (ICCV)_ , 2021, pp. 9650–9660.
  * [12] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in _Int Conf on Machine Learning (ICML)_ , 2020, pp. 1597–1607.
  * [13] T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton, “Big self-supervised models are strong semi-supervised learners,” in _Advances in Neural Information Processing Systems (NeurIPS)_ , 2020, pp. 22 243–22 255.
  * [14] X. Chen and K. He, “Exploring simple siamese representation learning,” in _IEEE Conf on Computer Vision and Pattern Recognition (CVPR)_ , 2021, pp. 15 750–15 758.
  * [15] J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar _et al._ , “Bootstrap your own latent-a new approach to self-supervised learning,” in _Advances in Neural Information Processing Systems (NeurIPS)_ , vol. 33, 2020, pp. 21 271–21 284.
  * [16] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in _IEEE Conf on Computer Vision and Pattern Recognition (CVPR)_ , 2020, pp. 9729–9738.
  * [17] P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” in _Advances in Neural Information Processing Systems (NeurIPS)_ , vol. 33, 2020, pp. 18 661–18 673.
  * [18] A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” _arXiv preprint arXiv:1807.03748_ , 2018.
  * [19] J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, “Signature verification using a” siamese” time delay neural network,” _Advances in neural information processing systems_ , vol. 6, 1993.
  * [20] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in _IEEE Conf on Computer Vision and Pattern Recognition (CVPR)_ , 2009, pp. 248–255.
  * [21] K. Chaitanya, E. Erdil, N. Karani, and E. Konukoglu, “Contrastive learning of global and local features for medical image segmentation with limited annotations,” in _Advances in Neural Inf Proc Systems (NeurIPS)_ , vol. 33, 2020, pp. 12 546–12 558.
  * [22] Y. Chen, C. Zhang, L. Liu, C. Feng, C. Dong, Y. Luo, and X. Wan, “USCL: Pretraining deep ultrasound image diagnosis model through video contrastive representation learning,” in _Int Conf on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2021, pp. 627–637.
  * [23] A. Gomariz, H. Lu, Y. Y. Li, T. Albrecht, A. Maunz, F. Benmansour, A. M. Valcarcel, J. Luu, D. Ferrara, and O. Goksel, “Unsupervised domain adaptation with contrastive learning for oct segmentation,” in _Int Conf on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_ , 2022, pp. 351–361.
  * [24] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in _Int Conf on Learning Representations (ICLR)_ , 2015.
  * [25] Y. Wu and K. He, “Group normalization,” in _European Conference on Computer Vision (ECCV)_ , 2018, pp. 3–19.
  * [26] O. Maier, B. H. Menze, J. von der Gablentz, L. Häni, M. P. Heinrich, M. Liebrand, S. Winzeck, A. Basit, P. Bentley, L. Chen _et al._ , “Isles 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral mri,” _Medical image analysis_ , vol. 35, pp. 250–269, 2017.
  * [27] A. Maunz, F. Benmansour, Y. Li, T. Albrecht, Y.-P. Zhang, F. Arcadu, Y. Zheng, S. Madhusudhan, and J. Sahni, “Accuracy of a machine-learning algorithm for detecting and classifying choroidal neovascularization on spectral-domain optical coherence tomography,” _Journal of Personalized Medicine_ , vol. 11, no. 6, p. 524, 2021.
  * [28] A. Gomariz, H. Lu, Y. Li, T. Albrecht, A. Maunz, F. Benmansour, J. Luu, O. Goksel, and D. Ferrara, “A unified deep learning approach for oct segmentation from different devices and retinal diseases,” _Investigative Ophthalmology & Visual Science_, vol. 63, no. 7, pp. 2053–F0042, 2022.

Supplementary Material

TABLE S1:  Datasets employed for the training and evaluation of models. Labeled data for training is displayed as #training+#validation. While the unlabeled data for $D_{2}$ and $D_{3}$ come from a database of numerous non-annotated volumes, the unlabeled data for $D_{1}$ is considered to be the unlabeled B-scans of the labeled volumes in order to enable evaluations also on that domain.  Domain | Device | Disease | Training | Testing
---|---|---|---|---
4-9 | Labeled | Unlabeled | Labeled
4-9 | B-scans | Volumes | B-scans | Volumes | B-scans | Volumes
$D_{1}$ | Spectralis | nAMD | 1363+243 | 234+41 | 11 466 | 275 | 163 | 28
$D_{2}$ | Cirrus | nAMD | 735+125 | 122+21 | $6.8\times 10^{6}$ | 53 197 | 99 | 17
$D_{3}$ | Spectralis | DME | 1264+226 | 228+40 | $1.1\times 10^{5}$ | 4098 | 196 | 35
TABLE S2:  Absolute segmentation metrics for different tissue classes in the
unsupervised domain adaptation setting with $D^{s}=D_{1}$ and $D^{t}=D_{2}$.

Model | Dice | UVD
---|---|---
2-33 | Target domain | Source domain | Target domain | Source domain
2-33 | IRF | SRF | PED | SHRM | IRF | SRF | PED | SHRM | IRF | SRF | PED | SHRM | IRF | SRF | PED | SHRM
UpperBound | 67.25$\pm$ | 1.66 | 59.64$\pm$ | 3.31 | 60.39$\pm$ | 1.70 | 57.94$\pm$ | 3.02 | N/A |  | N/A |  | N/A |  | N/A |  | 11.70$\pm$ | 0.36 | 9.78$\pm$ | 0.53 | 8.19$\pm$ | 0.46 | 8.77$\pm$ | 0.46 | N/A |  | N/A |  | N/A |  | N/A |
Baseline | 44.82$\pm$ | 4.90 | 44.22$\pm$ | 6.15 | 34.98$\pm$ | 7.72 | 32.91$\pm$ | 6.48 | 77.72$\pm$ | 2.07 | 63.64$\pm$ | 1.13 | 70.98$\pm$ | 0.57 | 56.20$\pm$ | 0.99 | 18.58$\pm$ | 0.92 | 16.70$\pm$ | 1.96 | 14.45$\pm$ | 2.19 | 16.14$\pm$ | 0.98 | 2.26$\pm$ | 0.11 | 8.15$\pm$ | 0.30 | 6.22$\pm$ | 0.47 | 7.38$\pm$ | 0.27
SimCLR | 47.23$\pm$ | 6.07 | 44.22$\pm$ | 7.29 | 37.56$\pm$ | 4.26 | 35.05$\pm$ | 4.13 | 76.67$\pm$ | 2.72 | 63.20$\pm$ | 1.93 | 70.74$\pm$ | 1.29 | 56.19$\pm$ | 2.40 | 17.37$\pm$ | 1.65 | 17.04$\pm$ | 1.65 | 14.02$\pm$ | 1.24 | 16.13$\pm$ | 0.98 | 2.31$\pm$ | 0.10 | 8.54$\pm$ | 0.39 | 6.04$\pm$ | 0.36 | 7.44$\pm$ | 0.35
SimSiam | 45.37$\pm$ | 3.83 | 48.94$\pm$ | 4.52 | 36.09$\pm$ | 5.45 | 34.17$\pm$ | 5.72 | 78.81$\pm$ | 1.94 | 63.26$\pm$ | 1.23 | 71.15$\pm$ | 0.77 | 56.37$\pm$ | 1.46 | 18.00$\pm$ | 0.98 | 16.30$\pm$ | 1.49 | 13.95$\pm$ | 1.27 | 15.66$\pm$ | 1.18 | 2.22$\pm$ | 0.07 | 8.54$\pm$ | 0.43 | 5.95$\pm$ | 0.44 | 7.42$\pm$ | 0.37
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{pool}}$) | 61.60$\pm$ | 6.64 | 60.55$\pm$ | 4.70 | 53.69$\pm$ | 2.61 | 56.54$\pm$ | 5.55 | 75.91$\pm$ | 4.55 | 63.60$\pm$ | 1.42 | 70.43$\pm$ | 1.13 | 59.05$\pm$ | 1.14 | 12.71$\pm$ | 1.41 | 10.49$\pm$ | 1.11 | 12.22$\pm$ | 2.47 | 11.66$\pm$ | 1.32 | 2.56$\pm$ | 0.06 | 8.87$\pm$ | 0.52 | 5.51$\pm$ | 0.27 | 6.98$\pm$ | 0.57
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) | 34.78$\pm$ | 4.71 | 35.46$\pm$ | 7.18 | 44.99$\pm$ | 9.47 | 42.32$\pm$ | 7.45 | 52.19$\pm$ | 6.97 | 47.70$\pm$ | 3.00 | 62.03$\pm$ | 2.80 | 47.03$\pm$ | 2.97 | 18.03$\pm$ | 1.86 | 17.68$\pm$ | 4.50 | 15.66$\pm$ | 5.97 | 17.63$\pm$ | 9.65 | 2.98$\pm$ | 0.13 | 11.31$\pm$ | 0.54 | 9.61$\pm$ | 0.98 | 9.25$\pm$ | 0.84
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{pool}}$) | 64.00$\pm$ | 6.72 | 59.16$\pm$ | 2.88 | 56.50$\pm$ | 3.98 | 58.79$\pm$ | 5.43 | 75.51$\pm$ | 3.86 | 62.79$\pm$ | 1.70 | 71.30$\pm$ | 1.37 | 57.97$\pm$ | 1.18 | 12.49$\pm$ | 0.80 | 10.67$\pm$ | 1.49 | 10.99$\pm$ | 2.03 | 12.37$\pm$ | 3.20 | 2.63$\pm$ | 0.13 | 8.64$\pm$ | 0.49 | 5.56$\pm$ | 0.28 | 7.06$\pm$ | 0.42
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) | 61.29$\pm$ | 7.87 | 58.30$\pm$ | 3.06 | 57.09$\pm$ | 4.06 | 56.62$\pm$ | 4.57 | 76.22$\pm$ | 5.23 | 63.20$\pm$ | 1.84 | 71.31$\pm$ | 2.11 | 61.01$\pm$ | 2.12 | 13.07$\pm$ | 1.75 | 10.76$\pm$ | 2.12 | 10.96$\pm$ | 2.45 | 11.70$\pm$ | 2.79 | 2.52$\pm$ | 0.12 | 8.50$\pm$ | 0.58 | 5.40$\pm$ | 0.23 | 6.96$\pm$ | 0.41
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{ch}}$) | 52.76$\pm$ | 10.43 | 46.87$\pm$ | 10.14 | 51.74$\pm$ | 3.14 | 51.03$\pm$ | 10.30 | 74.25$\pm$ | 2.87 | 62.46$\pm$ | 0.89 | 71.44$\pm$ | 0.82 | 57.36$\pm$ | 1.32 | 14.76$\pm$ | 4.17 | 15.02$\pm$ | 4.66 | 11.48$\pm$ | 2.55 | 11.74$\pm$ | 1.35 | 2.33$\pm$ | 0.08 | 8.50$\pm$ | 0.29 | 6.02$\pm$ | 0.31 | 7.66$\pm$ | 0.26
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) | 62.44$\pm$ | 4.73 | 59.30$\pm$ | 6.18 | 55.86$\pm$ | 2.05 | 54.78$\pm$ | 6.01 | 75.85$\pm$ | 3.82 | 63.79$\pm$ | 1.51 | 69.67$\pm$ | 1.96 | 59.86$\pm$ | 2.80 | 13.20$\pm$ | 1.81 | 10.75$\pm$ | 1.49 | 11.12$\pm$ | 2.30 | 11.17$\pm$ | 1.69 | 2.60$\pm$ | 0.17 | 8.81$\pm$ | 0.64 | 5.52$\pm$ | 0.39 | 7.37$\pm$ | 0.49

TABLE S3:  Absolute segmentation metrics for different tissue classes in the
unsupervised domain adaptation setting with $D^{s}=D_{1}$ and $D^{t}=D_{3}$.

Model | Dice | UVD
---|---|---
2-25 | Target domain | Source domain | Target domain | Source domain
2-25 | IRF | SRF | IRF | SRF | PED | SHRM | IRF | SRF | IRF | SRF | PED | SHRM
UpperBound | 65.45$\pm$ | 1.09 | 93.18$\pm$ | 0.85 | N/A |  | N/A |  | N/A |  | N/A |  | 11.36$\pm$ | 0.23 | 1.79$\pm$ | 0.17 | N/A |  | N/A |  | N/A |  | N/A |
Baseline | 53.84$\pm$ | 3.26 | 75.71$\pm$ | 3.64 | 77.72$\pm$ | 2.07 | 63.64$\pm$ | 1.13 | 70.98$\pm$ | 0.57 | 56.20$\pm$ | 0.99 | 16.23$\pm$ | 0.99 | 2.53$\pm$ | 0.24 | 2.26$\pm$ | 0.11 | 8.15$\pm$ | 0.30 | 6.22$\pm$ | 0.47 | 7.38$\pm$ | 0.27
SimCLR | 53.42$\pm$ | 2.07 | 57.24$\pm$ | 7.70 | 69.80$\pm$ | 3.35 | 61.38$\pm$ | 2.30 | 68.76$\pm$ | 1.09 | 54.73$\pm$ | 1.26 | 16.63$\pm$ | 0.78 | 3.63$\pm$ | 0.59 | 2.62$\pm$ | 0.11 | 8.52$\pm$ | 0.34 | 6.51$\pm$ | 0.45 | 7.62$\pm$ | 0.27
SimSiam | 54.71$\pm$ | 3.69 | 76.70$\pm$ | 5.77 | 76.46$\pm$ | 2.89 | 63.21$\pm$ | 1.18 | 71.18$\pm$ | 0.88 | 56.76$\pm$ | 1.61 | 15.88$\pm$ | 0.82 | 2.54$\pm$ | 0.41 | 2.30$\pm$ | 0.12 | 8.31$\pm$ | 0.32 | 5.83$\pm$ | 0.37 | 7.37$\pm$ | 0.26
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{pool}}$) | 58.31$\pm$ | 5.09 | 86.68$\pm$ | 4.53 | 78.62$\pm$ | 1.93 | 63.55$\pm$ | 1.98 | 71.17$\pm$ | 1.65 | 60.60$\pm$ | 2.40 | 13.65$\pm$ | 1.55 | 2.24$\pm$ | 0.54 | 2.53$\pm$ | 0.09 | 8.61$\pm$ | 0.69 | 5.29$\pm$ | 0.18 | 7.01$\pm$ | 0.36
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{pool}}$) | 50.46$\pm$ | 2.64 | 75.79$\pm$ | 12.31 | 67.74$\pm$ | 6.05 | 61.00$\pm$ | 2.09 | 70.09$\pm$ | 1.49 | 53.19$\pm$ | 1.62 | 17.24$\pm$ | 0.56 | 3.75$\pm$ | 2.09 | 2.55$\pm$ | 0.11 | 8.54$\pm$ | 0.84 | 6.05$\pm$ | 0.24 | 7.85$\pm$ | 0.56
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{pool}}$) | 56.45$\pm$ | 4.12 | 86.51$\pm$ | 4.18 | 78.03$\pm$ | 2.16 | 64.48$\pm$ | 2.08 | 71.06$\pm$ | 1.62 | 60.98$\pm$ | 2.91 | 14.12$\pm$ | 0.85 | 2.79$\pm$ | 0.71 | 2.50$\pm$ | 0.09 | 8.55$\pm$ | 0.84 | 5.33$\pm$ | 0.46 | 6.94$\pm$ | 0.55
SegCLR($P_{\mathrm{a}}$,$C_{\mathrm{ch}}$) | 58.15$\pm$ | 3.17 | 88.89$\pm$ | 3.21 | 77.82$\pm$ | 2.38 | 63.14$\pm$ | 1.86 | 70.92$\pm$ | 2.14 | 61.11$\pm$ | 2.04 | 13.59$\pm$ | 0.76 | 2.10$\pm$ | 0.54 | 2.51$\pm$ | 0.12 | 8.83$\pm$ | 0.61 | 5.23$\pm$ | 0.18 | 7.16$\pm$ | 0.47
SegCLR($P_{\mathrm{s}}$,$C_{\mathrm{ch}}$) | 56.17$\pm$ | 2.47 | 73.84$\pm$ | 8.96 | 73.78$\pm$ | 3.06 | 61.66$\pm$ | 2.36 | 70.74$\pm$ | 0.95 | 57.21$\pm$ | 2.02 | 16.30$\pm$ | 0.69 | 3.33$\pm$ | 0.63 | 2.32$\pm$ | 0.08 | 8.81$\pm$ | 0.29 | 6.06$\pm$ | 0.54 | 7.62$\pm$ | 0.37
SegCLR($P_{\mathrm{s+a}}$,$C_{\mathrm{ch}}$) | 58.39$\pm$ | 3.46 | 89.60$\pm$ | 3.86 | 79.10$\pm$ | 2.48 | 63.72$\pm$ | 1.71 | 70.98$\pm$ | 1.84 | 60.88$\pm$ | 2.53 | 13.13$\pm$ | 0.62 | 1.99$\pm$ | 0.68 | 2.58$\pm$ | 0.20 | 8.32$\pm$ | 0.60 | 5.36$\pm$ | 0.59 | 7.10$\pm$ | 0.66

TABLE S4:  Zero-shot DA results for models evaluated on $D_{1}$ after training
on different $D^{s}$. Absolute metrics are shown for each of the evaluated
classes.

$D^{s}$ | Model | Dice | UVD
---|---|---|---
3-18 | IRF | SRF | PED | SHRM | IRF | SRF | PED | SHRM
$D_{\textrm{1}}$ | Baseline | 77.72$\pm$ | 2.07 | 63.64$\pm$ | 1.13 | 70.98$\pm$ | 0.57 | 56.20$\pm$ | 0.99 | 2.26$\pm$ | 0.11 | 8.15$\pm$ | 0.30 | 6.22$\pm$ | 0.47 | 7.38$\pm$ | 0.27
SegCLR | 80.51$\pm$ | 1.89 | 64.25$\pm$ | 1.55 | 71.35$\pm$ | 1.15 | 60.43$\pm$ | 2.43 | 2.43$\pm$ | 0.10 | 8.89$\pm$ | 0.66 | 5.31$\pm$ | 0.21 | 7.14$\pm$ | 0.50
$D_{\textrm{2}}$ | Baseline | 43.08$\pm$ | 7.38 | 45.44$\pm$ | 4.99 | 51.56$\pm$ | 4.23 | 43.94$\pm$ | 3.66 | 4.24$\pm$ | 0.77 | 15.66$\pm$ | 2.33 | 10.21$\pm$ | 0.77 | 13.19$\pm$ | 3.55
SegCLR | 57.35$\pm$ | 10.06 | 54.06$\pm$ | 4.21 | 50.36$\pm$ | 3.66 | 56.44$\pm$ | 2.38 | 3.54$\pm$ | 0.41 | 10.54$\pm$ | 2.16 | 10.38$\pm$ | 3.94 | 8.48$\pm$ | 0.47
$D_{\textrm{3}}$ | Baseline | 46.46$\pm$ | 4.12 | 40.71$\pm$ | 0.81 | N/ | A | N/ | A | 7.19$\pm$ | 1.46 | 12.24$\pm$ | 0.30 | N/ | A | N/ | A
SegCLR | 41.58$\pm$ | 6.57 | 38.63$\pm$ | 3.21 | N/ | A | N/ | A | 8.57$\pm$ | 3.56 | 13.42$\pm$ | 0.79 | N/ | A | N/ | A
$D_{\mathrm{All}}$ | Baseline | 77.41$\pm$ | 1.94 | 64.76$\pm$ | 0.81 | 70.83$\pm$ | 0.84 | 57.87$\pm$ | 1.02 | 2.33$\pm$ | 0.10 | 7.88$\pm$ | 0.15 | 5.85$\pm$ | 0.33 | 7.15$\pm$ | 0.10
SegCLR | 79.84$\pm$ | 3.07 | 64.05$\pm$ | 2.18 | 72.42$\pm$ | 1.53 | 63.14$\pm$ | 2.31 | 2.35$\pm$ | 0.09 | 8.54$\pm$ | 0.53 | 5.01$\pm$ | 0.21 | 7.14$\pm$ | 0.53

TABLE S5:  Zero-shot DA results for models evaluated on $D_{2}$ after training
on different $D^{s}$. Absolute metrics are shown for each of the evaluated
classes.

$D^{s}$ | Model | Dice | UVD
---|---|---|---
3-18 | IRF | SRF | PED | SHRM | IRF | SRF | PED | SHRM
$D_{\textrm{1}}$ | Baseline | 44.82$\pm$ | 4.90 | 44.22$\pm$ | 6.15 | 34.98$\pm$ | 7.72 | 32.91$\pm$ | 6.48 | 18.58$\pm$ | 0.92 | 16.70$\pm$ | 1.96 | 14.45$\pm$ | 2.19 | 16.14$\pm$ | 0.98
SegCLR | 59.66$\pm$ | 7.82 | 52.37$\pm$ | 10.61 | 49.93$\pm$ | 3.99 | 50.33$\pm$ | 6.67 | 13.77$\pm$ | 1.54 | 12.21$\pm$ | 2.92 | 12.54$\pm$ | 2.27 | 11.94$\pm$ | 1.86
$D_{\textrm{2}}$ | Baseline | 67.25$\pm$ | 1.66 | 59.64$\pm$ | 3.31 | 60.39$\pm$ | 1.70 | 57.94$\pm$ | 3.02 | 11.70$\pm$ | 0.36 | 9.78$\pm$ | 0.53 | 8.19$\pm$ | 0.46 | 8.77$\pm$ | 0.46
SegCLR | 71.10$\pm$ | 2.44 | 58.19$\pm$ | 5.28 | 66.71$\pm$ | 3.26 | 60.34$\pm$ | 2.70 | 10.95$\pm$ | 0.54 | 10.03$\pm$ | 0.78 | 7.35$\pm$ | 0.38 | 8.17$\pm$ | 0.77
$D_{\textrm{3}}$ | Baseline | 41.52$\pm$ | 2.04 | 29.01$\pm$ | 5.48 | N/ | A | N/ | A | 24.10$\pm$ | 3.94 | 21.78$\pm$ | 1.39 | N/ | A | N/ | A
SegCLR | 45.37$\pm$ | 6.81 | 35.62$\pm$ | 9.37 | N/ | A | N/ | A | 28.98$\pm$ | 12.93 | 21.27$\pm$ | 5.30 | N/ | A | N/ | A
$D_{\mathrm{All}}$ | Baseline | 70.81$\pm$ | 2.14 | 66.16$\pm$ | 1.84 | 66.49$\pm$ | 1.64 | 61.53$\pm$ | 2.16 | 9.58$\pm$ | 0.38 | 8.34$\pm$ | 0.33 | 7.38$\pm$ | 0.36 | 7.75$\pm$ | 0.47
SegCLR | 74.27$\pm$ | 1.92 | 67.80$\pm$ | 4.46 | 71.05$\pm$ | 2.72 | 64.80$\pm$ | 3.22 | 9.58$\pm$ | 0.70 | 8.66$\pm$ | 0.38 | 6.98$\pm$ | 0.69 | 7.52$\pm$ | 0.37

TABLE S6:  Zero-shot DA results for models evaluated on $D_{3}$ after training
on different $D^{s}$. Absolute metrics are shown for each of the evaluated
classes. Note that PED and SHRM are not relevant for $D_{3}$ (see Section
III).

$D^{s}$ | Model | Dice | UVD
---|---|---|---
3-10 | IRF | SRF | IRF | SRF
$D_{\textrm{1}}$ | Baseline | 53.84$\pm$ | 3.26 | 75.71$\pm$ | 3.64 | 16.23$\pm$ | 0.99 | 2.53$\pm$ | 0.24
SegCLR | 54.69$\pm$ | 2.88 | 87.58$\pm$ | 3.58 | 14.17$\pm$ | 0.79 | 2.37$\pm$ | 0.66
$D_{\textrm{2}}$ | Baseline | 29.91$\pm$ | 6.51 | 51.52$\pm$ | 15.63 | 22.06$\pm$ | 1.44 | 6.85$\pm$ | 1.85
SegCLR | 34.81$\pm$ | 5.29 | 75.41$\pm$ | 12.11 | 19.14$\pm$ | 1.08 | 3.26$\pm$ | 1.68
$D_{\textrm{3}}$ | Baseline | 66.18$\pm$ | 1.61 | 92.73$\pm$ | 0.66 | 11.19$\pm$ | 0.21 | 1.85$\pm$ | 0.17
SegCLR | 67.76$\pm$ | 0.71 | 92.65$\pm$ | 3.62 | 10.62$\pm$ | 0.44 | 1.50$\pm$ | 0.23
$D_{\mathrm{All}}$ | Baseline | 66.54$\pm$ | 1.27 | 92.27$\pm$ | 1.13 | 11.23$\pm$ | 0.28 | 1.49$\pm$ | 0.14
SegCLR | 68.61$\pm$ | 1.00 | 94.17$\pm$ | 1.57 | 10.32$\pm$ | 0.17 | 1.24$\pm$ | 0.26