# ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale
Residual Connections for Medical Image Segmentation

Tariq M. Khan, Syed S. Naqvi, and Erik Meijering Tariq M. Khan and Erik
Meijering are with the School of Computer Science and Engineering, University
of New South Wales, Sydney, NSW, AustraliaSyed S. Naqvi is with the Department
of Electrical and Computer Engineering, COMSATS University Islamabad,
Pakistan.

###### Abstract

Segmentation is an important task in a wide range of computer vision
applications, including medical image analysis. Recent years have seen an
increase in the complexity of medical image segmentation approaches based on
sophisticated convolutional neural network architectures. This progress has
led to incremental enhancements in performance on widely recognised benchmark
datasets. However, most of the existing approaches are computationally
demanding, which limits their practical applicability. This paper presents an
expand-squeeze dual multiscale residual network (ESDMR-Net), which is a fully
convolutional network that is particularly well-suited for resource-
constrained computing hardware such as mobile devices. ESDMR-Net focusses on
extracting multiscale features, enabling the learning of contextual
dependencies among semantically distinct features. The ESDMR-Net architecture
allows dual-stream information flow within encoder-decoder pairs. The
expansion operation (depthwise separable convolution) makes all of the rich
features with multiscale information available to the squeeze operation
(bottleneck layer), which then extracts the necessary information for the
segmentation task. The Expand-Squeeze (ES) block helps the network pay more
attention to under-represented classes, which contributes to improved
segmentation accuracy. To enhance the flow of information across multiple
resolutions or scales, we integrated dual multiscale residual (DMR) blocks
into the skip connection. This integration enables the decoder to access
features from various levels of abstraction, ultimately resulting in more
comprehensive feature representations. We present experiments on seven
datasets from five distinct examples of applications: segmentation of retinal
vessels ($2\times)$, skin lesions ($2\times)$, digestive tract polyps, lung
regions, and cells. Our model demonstrates strong performance, with an F1
score of 0.8287%, 0.8211%, 0.9034%, 0.9451%, 0.9543%, 0.9840%, and 0.8424% on
the DRIVE, CHASE, ISIC2017, ISIC2016, CVC-ClinicDB, MC and MoNuSeg datasets,
respectively. Remarkably, our model achieves these results despite having
significantly fewer trainable parameters, with a reduction of two or even
three orders of magnitude.

###### Index Terms:

Medical Image Segmentation, Deep Neural Networks, Lightweight Networks,
Resource-Constrained Networks

##  I Introduction

The accurate segmentation of anatomical structures and abnormalities in
medical images plays a vital role in the diagnosis and, ultimately, in the
provision of appropriate treatment for patients. The segmentation process can
be complex, even for highly experienced human experts in the field [1, 2, 3,
4]. Challenges arise due to factors such as ambiguous structural boundaries,
inherent uncertainty in segmented regions, diverse textures, nonuniform
intensity distribution, and significant contrast variations commonly
encountered in medical images [5, 6, 7, 8, 9, 10]. These complexities
emphasise the importance of developing advanced segmentation techniques to
facilitate clinical diagnosis.

Current techniques for medical image segmentation can be broadly categorised
into unsupervised and supervised approaches. Unsupervised approaches often
rely on low-level features and ad-hoc rules, which can lead to limited
generalisation capabilities. In contrast, supervised methods use human-
annotated training images, which typically leads to superior segmentation
accuracy compared to unsupervised techniques. Deep learning has emerged as the
most effective among supervised approaches, facilitating end-to-end
segmentation with enhanced accuracy and generalisation. Several convolutional
neural networks (CNNs) have been specifically designed and developed for
medical image segmentation, demonstrating the versatility and potential of
these techniques within the field of medical imaging [5, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20].

To maintain spatial localisation information, fully convolutional networks
(FCNs) with skip layers were suggested for semantic segmentation [21]. U-Net,
which was inspired by FCNs, has become a prominent network architecture for a
wide range of segmentation applications [22]. CNN models that learn new
information through a reinforcement strategy technique have also been proposed
[23]. The introduction of a connection-sensitive U-Net has further improved
the segmentation results [24]. Using a combination of higher resolution
features and upsampled features, it makes use of contextual information from
deeper layers. A joint loss at the segmentation level and at the pixel level
has been proposed to address this problem [25]. An evaluation metric for
skeleton similarity has been developed to evaluate the skeleton map generated
by combining reference and source segmentation maps [26].

In situations where computing resources are limited, it becomes crucial to
minimise the number of trainable parameters. For example, in point-of-care
(POC) diagnostics, which aims to perform medical tests at or near the point in
time and space where the patient receives care, there may only be room for
lightweight devices, such as smartphones [27]. Smartphone-based POC
diagnostics and decision support systems have been explored for the analysis
of the retinal vasculature and the diagnosis of retinal disease [28, 29].
However, current state-of-the-art methods for medical image segmentation,
including encoder-decoder-based networks and transformer-based methods, are
not practical in POC diagnostics, as they often require excessive computing
resources.

One approach to reducing the number of trainable parameters is to reduce the
number of filters used in each layer of the network and/or the number of
hidden layers. Consequently, shallow networks often emerge as a feasible
alternative to their deep-network counterparts [30]. These networks can
provide a balance between computational efficiency and performance, making
them suitable for applications on devices with limited resources. However, the
feature maps they produce may contain overlapping information if the variation
in the features is low compared to the number of filters employed [31].

In the literature, a less complex alternative is to use lightweight networks
[32, 12]. To comply with hardware limitations, these networks often reduce the
number of filters per layer [32]. For example, MobileNet-V3 [32] has a large
number of pooling kernels and a large stride, which limit its memory footprint
but contribute to its low performance when segmenting high-frequency features.
T-Net [12], GLAN [16], G-Net [13], PLVS-Net [9], and MKIS-Net [14], on the
other hand, employ small filter kernels to extract low-frequency information,
but due to their shallow structures, they perform poorly when dealing with
datasets that exhibit significant feature variation. Hence, an architecture is
required that can extract robust features for all inherent frequencies in
medical images, encode feature variations for varying datasets and conform
with the computational requirements of POC diagnosis.

This paper presents a novel, efficient and lightweight design that we refer to
as ESDMR-Net, short for the expand-squeeze dual multiscale residual network.
ESDMR-Net surpasses MobileNet-V3 in successfully capturing the essential high-
frequency features necessary for medical image segmentation, thus overcoming
the shortcomings of MobileNet-V3 in this aspect. ESDMR-Net also outperforms
T-Net by solving the latter’s shortcomings in managing large feature
variation, providing a more flexible solution for a wider range of datasets.
ESDMR-Net is a generic architecture designed specifically for environments
with limited memory and computing resources.

The proposed network design involves expand-squeeze (ES) blocks that are used
on multiple scales to capture high-frequency information and cross-channel
correlations between features to exploit class information and promote under-
represented classes, while separable convolution in depth is used in the ES
blocks to accumulate more useful features with low computational requirements.
To ensure that rich feature representations are preserved in the decoder, a
dual multiscale residual (DMR) block is proposed at the skip connections. DMR
blocks in skip connections facilitate effective transmission of gradient
information on multiple scales so that feature information is preserved at the
decoder end. The DMR expands the receptive field of the decoder, allowing it
to access more contextual information from different levels of the encoder.
The DMR blocks aim to address challenges such as loss of feature information
in the transmission process and the vanishing gradient problem, and they
improve the network’s ability to capture and represent information from
different levels of abstraction. To deal with highly varying features, the
network is designed to have a deep structure by utilising a large number of
branches and the reuse of ES blocks so that the refinement of the features can
be accumulated through the network.

The proposed ESDMR-Net design has only around 0.7 million parameters, which is
the same as M2U-Net but five times less than MobileNet-V3. ESDMR-Net overcomes
the limitations of existing lightweight networks such as MobileNet-V3 [32] and
T-Net [12]. It is a generic architecture designed specifically for
environments with limited memory and computing resources.

The main contributions are as follows:

  * •

A novel deep convolutional network architecture with ES blocks is proposed for
improved feature representation to cater high frequency and feature variations
in medical images.

  * •

The use of skip connections and DMR blocks enable feature preservation at the
decoder end, gradient propagation, preservation of contextual information, and
allows fine-grained feature reuse.

  * •

The deep structure of the network allows it to be robust to highly varying
features via feature refinement and accumulation.

  * •

The suggested framework is tailored for limited resource settings, boasting a
low resource footprint, yet it maintains competitive performance compared to
existing methodologies.

  * •

The proposed network has been evaluated on five different applications and
seven distinct datasets to show its robustness and generalizability.

The remainder of the paper is organised as follows: Section II discusses
recent relevant works. The design and technical details of the proposed ESDMR-
Net are provided in Section III. The experimental setup is summarised in
Section IV. The detailed experimental results of ESDMR-Net and comparisons
with other methods are presented in Section V. Finally, Section VI discusses
the main findings and conclusions of the proposed work.

##  II Related Work

Before presenting our proposed method, we first provide a concise overview of
state-of-the-art methods that serve as benchmarks for evaluating our method.
Our evaluation spans diverse applications, ensuring comprehensive comparisons
based on the performances reported by both original developers and existing
literature.

Among the lightweight architectures, [33] presented a fully convolutional low
parameter network (FCN) that, while efficient, can experience information loss
during upsampling. In [12] a reduced complexity architecture, T-Net was
proposed that introduced a trade-off between representational learning and
computational overhead. MobileNet-V3, a platform-aware network that uses the
NetAdapt algorithm, was introduced in [32]. In [13], G-Net, a lightweight
variant of GoogleNet, was suggested that emphasises the preservation of
spatial context and the reduction of complexity. An encoder-decoder based
lightweight architecture named GLAN was proposed in [16]. MKIS-Net [14], a
lightweight network designed for image segmentation, employed multiple kernels
to create an effective receptive field, thus enhancing the performance of
segmentation.  These architectures optimise for reduced complexity, balancing
the trade-off between model size and performance. However, segmentation
quality can be compromised if information is lost during upsampling in
lightweight architectures such as the lightweight FCN [33]. The effectiveness
of lightweight architectures may depend on the task and may not outperform
more complex models in all scenarios.

The encoder-decoder category showcases notable designs such as SegNet [34],
which employs an encoder-decoder structure with skip connections for pixel-
wise classification. Classic U-Net [35] effectively captures the global and
local context through skip connections, maintaining fine details. U-Net++, an
extension of UNet, improves segmentation accuracy by nesting densely connected
pathways. MobileNetV2 components were leveraged in the encoder-decoder
architecture of M2U-Net by [36]. Attention gates were incorporated into
Attention Res-UNet to selectively emphasise relevant features [37]. MMS-Net
[15] is specifically designed for pixel-level detection, incorporating
multipath and multiscale convolutional operations, as well as multiple deep
feature aggregation techniques. PLVS-Net was proposed for accurate
segmentation of retinal vessels by [9]. Using prompt blocks, PLVS-Net achieves
improved feature extraction and segmentation performance with fewer than a
million parameters, making it suitable for resource-constrained devices. The
prompt blocks extract and preserve useful features and relay them to the
decoder to bridge the semantic gap between the encoder and the decoder. RCED-
Net, which addresses the segmentation issue by approaching it as a pixel-wise
semantic segmentation task, was proposed by [34]. A modified version of the
U-Net framework for performing segmentation on chest radiographs was proposed
by [38]. Subsequently, segmented images were used for classification purposes
to assess the resulting influence on performance. The segmentation model
proposed by [39] is based on an attention U-Net with three separate
convolutional neural networks (CNNs) backbones: visual geometry group 19
(VGG19), Inception-v3, and residual neural network 50 (ResNet50). A U-Net
model with spatial attention (SA-UNet) architecture was proposed for the
segmentation of chest X-ray (CXR) images by [40]. Attention U-Net models with
three architectures of convolutional neural networks (CNN), namely
Inception-v3, Visual Geometry Group 19 (VGG19) and Residual Neural Network 50
(ResNet50), were proposed for segmentation of fundus images by [41]. Edward’s
probabilistic programming language, together with the U-Net model and
adversarial generative networks employing different optimisers, was proposed
by [42] for ultrasound nerve segmentation. These encoder-decoder designs can
capture global and local context effectively, providing good pixel-wise
classification. Handling the semantic gap between encoders and decoders can be
difficult, limiting the model’s capacity to capture complex features. Most of
these systems are highly dependent on skip connections, which can cause
difficulty in obtaining detailed features in some cases.

The integration of contextual information was explored by [43] with DEU-Net,
which combines dual encoders with spatial and context paths, employing feature
fusion and channel attention for improved accuracy. Vessel-Net [44], used
multiscale multipath supervision with initial residual blocks for enhanced
segmentation. SFA [45] is characterised by selective aggregation of features
through common and paired decoders. An MS-NFN network, proposed by [46],
incorporates multiscale information at the patch level to enhance performance.
In [47], a semantic segmentation network is proposed that uses a dual-
residual-stream-based architecture. The first stream is a sequential path that
connects adjacent encoder and decoder layers with non-identity mappings, while
the second stream is an external path that directly connects encoder and
decoder layers to capture edge information (high-frequency spatial features).
One advantage of these methods that use contextual information is improved
segmentation accuracy. However, as observed in DEU-Net, the additional
complexity of contextual information integration could influence real-time
processing and require more computational resources. The nature of the
segmentation challenge can influence the effectiveness of tasks that involve
feature aggregation and multiscale information.

The pyramidal architecture, proposed by [48], was employed for the effective
integration of global and multiscale context information. DAGAN-Net, proposed
by [49], comprises two main modules: the UNet-SCDC segmentation module and the
DD module. FAT-Net [50] enhances overall performance by activating key
channels and suppressing irrelevant background noise. For effective and
precise segmentation of skin lesions, [51] proposed a novel network
architecture known as Ms RED. A Swin-UNet [52] uses a transformer-based
architecture for medical image segmentation. A novel segmentation scheme known
as UCTransNet [53], which incorporates a unique CTrans module into U-Net,
focussing on the channel perspective and employing an attention mechanism.
Pyramidal designs can effectively combine global and multiscale context
information, resulting in enhanced segmentation. Pyramidal structures may have
a high computational cost, which limits their use in resource-constrained
contexts. Specialised techniques may excel at specific tasks, but may lack
adaptability when applied to a wider range of segmentation challenges.

DRIU [54] is designed specifically for the segmentation of vascular networks
and optical discs, incorporating specialised layers. PraNet [55] used a
parallel reverse attention network for precise polyp segmentation, to improve
the precision of segmentation. AS-Net [56] combines spatial and channel
attention mechanisms to enhance the performance of segmentation for skin
lesions. The innovative BiO-Net [57] used a bidirectional O-shape network that
cyclically employs its building blocks, offering versatile domain enhancement.
ERFNet, proposed by [58], was designed for the semantic segmentation of real-
world images with a large number of classes. The network employs a significant
number of convolutional filters to represent the features at each stage. AACA-
MLA-D-Net, proposed by [59], efficiently leverages low-level details and
complementary information from multiple layers to accurately differentiate
vessels from the background while maintaining minimal model complexity.

These methodological variations provide a comprehensive foundation for
evaluating our proposed method’s performance across diverse domains.

##  III Proposed Network Design

The proposed ESDMR-Net encoder-decoder architecture (Fig. 1) has been
specifically designed to extract, enhance, and retain comprehensive feature
details at each phase of the encoding-decoding process, while taking into
account computational budget constraints, without compromising performance.
The extracted features can be broadly categorised into low-level (multiscale
frequency), mid-level (class-specific), and high-level (broader contextual
information) features. Using a compound scaling approach [60], we constructed
our network to be deep, with more than 100 layers. The ESDMR-Net comprises
four encoder and four matching decoder blocks. It captures multiscale
information through receptive fields of different scales. Average pooling is
used to downsample the feature maps in each encoder block, whereas bilinear
upsampling is used at the decoder end. Throughout the network, a pooling
kernel with dimensions of 3$\times$3 and a stride of 1 is utilised.

Figure 1: Block diagram of the proposed ESDMR-Net. The input is first
processed by an input block consisting of convolution (Conv), batch
normalisation, and rectified linear unit (ReLU) activation. Subsequently, four
encoder and four decoder blocks further extract multiscale information. The
final processing by an output block consisting of convolution, batch
normalisation, softmax activation, and Dice pixel classification produces the
output segmentation map. DMR blocks in the skip connections between the
encoder and decoder blocks, as well as the input and output blocks, facilitate
the preservation/restoration of information and training of the network.

The internal architecture of an ESDMR-Net encoder block has multiple branches
featuring separable depth convolution with varying receptive fields to capture
a rich collection of features of varying frequency. The rationale for
employing depthwise separable convolution is based on empirical evidence that
disentangling cross-channel correlations from spatial correlations can lead to
the extraction of more meaningful features [61]. Moreover, the ability of
depthwise separable convolution to capture more concepts compared to its
counterparts is also supported by previous work [61]. The network decoder
blocks follow the same topology as the encoder block, but with the
downsampling operation replaced by upsampling. A highlight of the architecture
is the ES block, which, unlike the Fire module [62] features an expansion
followed by a squeeze operation. The rationale behind this strategy is that
the expansion operation extracts important features containing multiscale
information, which can be subsequently used by the squeeze or bottleneck layer
to extract compact salient information.. Furthermore, overparameterization has
been shown to increase the efficiency of compact networks [63], and expanded
features improve the expressiveness of the network [64]. Prior work has
explored the ES convolution for superresolution [65, 66], which generally
features a $3\times 3$ convolution layer for expansion followed by a $1\times
1$ squeeze layer (bottleneck). In contrast, the proposed ES block employs
expansion layers of varying filter sizes to account for features occurring at
varying scales. Specifically, a depthwise separable convolution is employed
for increased expressiveness through expansion while simultaneously ensuring
compactness. In addition, the ES block incorporates nonlinear and
normalisation operations for nonlinearity training and faster convergence. The
proposed scale-aware approach is particularly well-suited to medical image
segmentation, where the regions of interest occur at varying scales and have
varied boundary structures.

Figure 2: Internal architecture of the ES block. The input is processed
successively by rectified linear unit (ReLU) activation, an expansion layer
implemented by depthwise separable convolution of specified scale $W\times W$
pixels, a squeeze layer implemented by identity mapping using $1\times 1$
convolution, and batch normalization. Figure 3: Schematic diagram of the dual
multiscale residual (DMR) block.

###  III-A Expand-Squeeze Block

The ES block consists of an expansion layer followed by a squeeze or the
representational bottleneck layer (Fig. 2). The expansion operation is
responsible for capturing spatial correlations and cross-channel correlations
between features. The squeeze layer then extracts the salient information in a
compact feature representation. By capturing the interdependencies among
channels, the proposed ES block enables the network to acquire more
distinctive features for each class and dynamically adjust the channel-wise
feature responses. The expansion operation enables the proposed ES block to
capture class-specific features, and the squeeze layer extracts salient
information to promote under-represented classes. This helps the proposed ES
module combat class imbalance issues. Therefore, the ES block used at
different spatial scales allows the network to balance both the feature
capacity and the expressiveness at the same time.

The proposed approach is different from the Fire module [62], as feature
correlations and multiscale information are learnt before transforming the
output to a new channel space. In contrast to the Inception module [67], the
expansion layer is implemented with depthwise separable convolution to extract
multiscale features with high representational power and low computational
overhead. By capturing the interdependencies among channels, the proposed ES
block enables the network to acquire more distinctive features and dynamically
adjust channel-wise feature responses. This helps to improve image
segmentation performance.

In this work, the expansion operation is performed by depthwise separable
convolution, where kernel sizes of $3\times 3$, $5\times 5$, and $7\times 7$
pixels are employed to capture spatial correlations at multiple scales. Let
${\widehat{K}}\in\mathbb{R}^{H\times W\times M}$ be a depthwise convolution
kernel, where $H$ and $W$ are the spatial dimensions of the kernel and $M$ is
the number of channels. The output ${D}\in\mathbb{R}^{K\times L\times M}$ of
the intermediate depthwise convolution given a feature map
${F}\in\mathbb{R}^{K\times L\times M}$ with spatial dimensions $K\times L$
pixels can be computed as:

${D}_{k,l,m}=\sum_{p,q}{\widehat{K}}_{p,q,m}{F}_{k-p,l-q,m},$ (1)

where a convolution filter per input channel is applied to obtain an
$M$-channel output. Given this output and multiple pointwise kernels,
${\dot{K}}_{n}\in\mathbb{R}^{1\times 1\times M}$, the output of the pointwise
convolution, which essentially computes a weighted linear combination of the
input channels, is defined as:

${E}_{k,l,n}=\sum_{m}{\dot{K}}_{m,n}{D}_{k,l,m}.$ (2)

To extract and consolidate spatial and channel information from the expansion
layers, a $1\times 1$ convolution is employed in our ES block, which is
similar to a bottleneck or squeeze operation. Both the expand and squeeze
operations enable the encoder block to encapsulate useful features with low
computational overhead.

###  III-B Dual Multiscale Residual Block

The DMR block (Fig. 3) is designed to extract and preserve features that
encompass a range of spatial scales, from fine-grained details to broader
contextual information. When used in conjunction with skip connections, it
serves as an effective method for feature extraction and refinement by
leveraging multiscale information. Inclusion of DMR blocks in skip connections
facilitates effective transmission of gradient information on multiple scales.
This assists in mitigating the vanishing gradient issue, consequently
enhancing the network’s convergence and stability. It also expands the
receptive field of the decoder, allowing it to access more contextual
information from different levels of the encoder. This helps in capturing
larger structures and contextually rich representations. DMR blocks facilitate
the reuse of features on different scales, allowing the network to take
advantage of local and global information for better feature representation.
The robustness of the features extracted by the proposed encoder and the
preservation of the features by using DMR blocks in skip connections are
supported by the results of our ablation study (Section V).

In this setup, the encoder’s feature maps before the max-pooling are fed as
input to the DMR block, whereas the skip connections enable the preservation
of essential information between layers. Given three layers of operation (Fig.
3), the internal processing of a DMR block can be defined as:

$\displaystyle T_{1}$ $\displaystyle=\beta_{n}\left(f^{3\times
3}_{1}\ast\mathcal{F}_{\text{in}}\right),$ (3) $\displaystyle T_{2}$
$\displaystyle=\beta_{n}\left(f^{5\times
5}_{1}\ast\mathcal{F}_{\text{in}}\right),$ (4) $\displaystyle T_{3}$
$\displaystyle=\Re\left(\beta_{n}\left(f^{3\times
3}_{2}\ast\left\\{T_{1},T_{2}\right\\}\right)\right),$ (5) $\displaystyle
T_{4}$ $\displaystyle=\Re\left(\beta_{n}\left(f^{5\times
5}_{2}\ast\left\\{T_{2},T_{1}\right\\}\right)\right),$ (6) $\displaystyle
T^{{}^{\prime}}$ $\displaystyle=f^{1\times
1}_{3}\ast\left\\{{T4,T3}\right\\},$ (7)

where $\mathcal{F}_{\text{in}}$ is the input feature tensor, $f$ represents
the convolution kernels with kernel size depicted as superscripts and layer
number as subscript, $\ast$ represents the convolution operation, $\beta_{n}$
denotes the batch normalization operation and the ReLU operation is depicted
by $\Re(x)=\max(0,x)$. Finally, $\left\\{\cdot,\cdot\right\\}$ denotes the
concatenation of features.

The unique dual-cross feature sharing of the proposed DMR block ensures robust
fusion and detection at multiple scales. The output of the DMR block is
computed as:

$\mathcal{F}_{\text{out}}=T^{{}^{\prime}}\bigoplus\;\Re\left(\beta_{n}\left(f^{1\times
1}_{2}\ast\mathcal{F}_{in}\right)\right),$ (8)

where $\bigoplus$ represents the tensor addition operation. Shortcut
connection $T_{5}$ improves network performance and eases training through
residual learning.

###  III-C Dice Similarity Coefficient Loss

A standard approach to deal with the inherent class imbalance nature of
medical images is the reweighting approach based on the frequency of pixels
[68]. This tackles the imbalance problem at a global level as opposed to a
local image-by-image level. Hence, we employ a local technique that maximises
the pixel-level Dice similarity coefficient (DSC) between the predictions and
the reference on an image-by-image basis. Accordingly, we formulate the loss
as

$\mathcal{L}_{\text{Dice}}=\sum_{{I}\in\mathcal{D}}(1-\text{DSC})^{2},$ (9)

where ${I}$ is an image in the training dataset $\mathcal{D}$ under
consideration. The DSC between the prediction $\hat{y}_{p}$ and the gold-
standard reference $g_{p}$ for the $p^{\text{th}}$ pixel can be given as

$\text{DSC}=\frac{2\sum_{p\in{I}}\hat{y}_{p}g_{p}}{\sum_{p\in{I}}{\hat{y}_{p}}^{2}+\sum_{p\in{I}}g_{p}^{2}},$
(10)

where $\hat{y}$ and $g$ are binary maps with foreground and background pixels
denoted by 1 and 0, respectively. Differentiating Eq. (9) yields the gradient
as follows:

$\nabla_{\hat{y}_{p}}\mathcal{L_{\text{Dice}}}=2(1-\text{DSC})\frac{\partial\text{DSC}}{\partial\hat{y}_{p}}.$
(11)

The partial derivative of DSC with respect to the $q^{\text{th}}$ pixel of the
prediction can be calculated as:

$\frac{\partial\text{DSC}}{\partial\hat{y}_{q}}={\frac{2{{g_{q}}\left({\displaystyle\sum_{p\in{I}}{\hat{y}_{p}^{2}\
+}\sum_{p\in{I}}{g_{p}^{2}}}\right)-4{\hat{y}_{q}}\left({\displaystyle\sum_{p\in{I}}{{\hat{y}_{p}}{g_{p}}}}\right)}}{{{{\left({\displaystyle\sum_{p\in{I}}{\hat{y}_{p}^{2}\
+}\sum_{p\in{I}}{g_{p}^{2}}}\right)}^{2}}}}}.$ (12)

##  IV Experimental Setup

The proposed network was evaluated on a variety of datasets from different
medical imaging modalities and domains to examine its broad applicability and
performance compared to the state-of-the-art. This section provides an
overview of the datasets, the specifics of the network implementation, the
performance metrics used, and the comparison methods.

###  IV-A Medical Image Datasets

Seven publicly available datasets from five medical image segmentation
applications were used to evaluate the performance of our network (Table I).
The five applications were segmentation of retinal vessels in fundus images
(fundus imaging), segmentation of skin lesions in clinical photographs
(optical imaging), segmentation of polyps in intestinal tract colon images
(endoscopy imaging), lung segmentation in chest radiographs (X-ray imaging)
and segmentation of cell nuclei in histopathological images (microscopic
imaging).

Application | Dataset | Image Resolution | Total | Training/Test Split
---|---|---|---|---
Retinal Vessels | DRIVE |  584$\times$565 | 40 | Train: 20, Test: 20
Retinal Vessels | CHASE |  999$\times$960 | 28 | Train: 70%, Test: 30%
Skin Lesions | ISIC 2016 |  679$\times$453–6,748$\times$4,499 | 1,279 | Train: 900, Test: 379
Skin Lesions | ISIC 2017 |  679$\times$453–6,748$\times$4,499 | 2,750 | Train: 2,000, Test: 600
Polyps | CVC-ClinicDB |  384$\times$288 | 612 | Train: 80%, Test: 10%
Lungs | Montgomery County (MC) |  4,892$\times$4,020–4,020$\times$4,892 | 138 | Train: 100, Test: 38
Cell Nuclei | MoNuSeg |  1,000$\times$1,000 pixels | 44 | Train: 30, Test: 14
TABLE I: Datasets used in the study.

For segmentation of retinal vessels, two publicly available retinal image
datasets (with a high frequency of containing tiny objects) were used, namely
DRIVE and CHASE [69]. The diabetic retinopathy screening programme that served
as the basis for the DRIVE dataset was carried out in the United Kingdom and
included patients of a wide age spectrum who were diabetic. It includes 20
images for training and 20 for testing, all sized at 584$\times$565 pixels.
Only seven out of the 40 images show minor indications of early diabetic
retinopathy. Gold-standard reference manual vessel segmentations by medical
experts are available for both training and testing images. The CHASE dataset
consists of 28 colour images taken from 14 English schoolchildren. Each image
has a resolution of 999$\times$960 pixels and a 30-degree FOV centered on the
optic disc. Two distinct manual segmentation maps are provided as a reference,
the first expert segmentation used as a reference for this study. The original
dataset does not have segregation of images into train and test sets. In our
study, the first 70% images were used for training, and the rest of the images
were used for performance evaluation.

For skin lesion segmentation, two datasets were used, namely the ISIC 2017
[70] and ISIC 2016 [71] datasets. The ISIC 2017 dataset contains 2,750 image
sizes ranging from $679\times 453$ to $6,748\times 4,499$ pixels, divided into
a training set of 2,000 images, a validation set of 150 images, and a test set
of 600 images. Similarly, the ISIC 2016 dataset comprises 1,279 images with
sizes ranging from $679\times 453$ to $6,748\times 4,499$ pixels. The dataset
is split into a training set consisting of 900 images and a test set
comprising 379 images.

The CVC-ClinicDB dataset [72] was employed for polyp segmentation. This
dataset consists of 612 images with a resolution of $384\times 288$ pixels,
each with a corresponding labelled map. Following the method used in earlier
studies [55, 73], we randomly chose 80% of the images to be used for training,
10% for testing, and the last 10% for validation.

The Montgomery County (MC) dataset [74] was used for lung segmentation. This
dataset consists of 138 frontal chest X-ray images specifically intended for
lung segmentation. These images are either 4,020$\times$4,892 or
4,892$\times$4,020 pixels in size and are presented in 12-bit greyscale. The
dataset includes 58 cases of tuberculosis and 80 cases of healthy individuals,
highlighting a wide range of anomalies.

The MoNuSeg dataset [75] comprises 30 training images and 14 test images, each
with a resolution of $1,000\times 1,000$ pixels. The images are sampled from a
diverse array of whole slide images from various organs, thus offering a broad
range of examples on which the machine learning model can be trained. To
supplement and enrich the dataset, a patch extraction strategy was
implemented. In this process, each image is divided to create four unique
patches, each of which has dimensions of 512$\times$512 pixels. Patches are
generated by extracting content from the four corners of each individual
image. This technique significantly increases the size of the original
dataset, multiplying it by a factor of four. As a result, instead of the
initial set of 44 images, we obtained a considerably expanded dataset
containing 176 patches, of which 120 were used for training and 56 for
testing.

###  IV-B Implementation Details

Our experiments were carried out on an Intel(R) Core(TM) i9-9900X CPU @
3.50GHz, equipped with 64 GB of RAM and an NVIDIA Quadro RTX 5000 GPU. We
employed the Adam optimisation algorithm with a learning rate of $1e^{-3}$.
The model’s objective function was the pixel-wise Dice loss function. To
address computational constraints, we employed a batch size of 8. Early
stopping was integrated to automatically determine the maximum number of
iterations, ensuring efficient training without overfitting. Our experiments
converged in a maximum of 15 epochs. Gradient clipping was applied using a
gradient threshold of 3 and the global $l_{2}$-norm [76]. This strategy
enabled effective experimentation even within the limitations of the available
computational resources.

###  IV-C Performance Measures

To assess segmentation performance, we employed several evaluation measures
[77]. Not all measures were used for the five applications considered in our
experiments. For each application, we used only those measures typically
reported in the literature for that application. Given the predicted image,
denoted by $P$, and the corresponding gold-standard reference image, denoted
by $R$, each consisting of $N$ pixels $i=1\dots N$ with values either 1
(positive $=$ foreground) or 0 (negative $=$ background), we initially
calculated the true-positives ($T_{P}$), true-negative ($T_{N}$), false-
positive ($F_{P}$), and false-negative ($F_{N}$) pixel counts:

$T_{P}=\sum^{N}_{i=1}P_{i}R_{i},$ (13)

$T_{N}=\sum^{N}_{i=1}(1-P_{i})(1-R_{i}),$ (14)

$F_{P}=\sum^{N}_{i=1}P_{i}(1-R_{i}),$ (15)

$F_{N}=\sum^{N}_{i=1}(1-P_{i})R_{i}.$ (16)

From these calculations, we obtain the sensitivity ($Se$), also known as the
Recall:

$Se=\frac{T_{P}}{T_{P}+F_{N}},$ (17)

the specificity ($Sp$):

$Sp=\frac{T_{N}}{T_{N}+F_{P}},$ (18)

the accuracy ($A$):

$A=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}},$ (19)

the $F_{1}$ score:

$F_{1}=\frac{2|P\cap R|}{|P|+|R|}=\frac{2T_{P}}{2T_{P}+F_{P}+F_{N}},$ (20)

and the Jaccard ($J$) coefficient:

$J=\frac{|P\cap R|}{|P\cup R|}=\frac{T_{P}}{T_{P}+F_{P}+F_{N}},$ (21)

where all values are within the range $[0,1]$, with 1 indicating optimal
performance and 0 representing the worst possible outcome. The area under the
curve (AUC) of the $Se$-$Sp$ plot is also sometimes used as a performance
measure.

An alternative to the $F_{1}$ score, used in some of the experiments, is the
weighted $F$ score:

$F_{\beta}^{w}=(1+\beta^{2})\frac{{\rm Precision}^{w}\cdot{\rm
Recall}^{w}}{\beta^{2}\,{\rm Precision}^{w}+{\rm Recall}^{w}},$ (22)

where ${\rm Precision}=T_{P}/(T_{P}+F_{P})$, ${\rm Recall}=Se$, $\omega$
assigns weights to different errors based on neighbourhood information, and
$\beta$ is a parameter to balance precision and recall. The weighted precision
measures the exactness, while the weighted recall measures the completeness
[78]. We gave equal importance to weighted precision and recall in our
evaluation, which means $\beta=1$. Additionally, to assess the deviation
between the predicted and the reference segmentation at the pixel level, the
mean absolute error (MAE) was employed:

$\textit{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|P_{i}-R_{i}\right|.$ (23)

For global-level pixel similarity, the enhanced alignment measure
$E_{\phi}^{\max}$ was used [79], given as:

$E_{\phi}^{\max}=\frac{1}{KL}\sum_{p=1}^{K}\sum_{q=1}^{L}\phi(p,q),$ (24)

where $\phi=f(\Lambda)$, with $f$ being a quadratic function of the alignment
matrix $\Lambda$ [79]. Finally, to quantify structural similarities (SSIM)
between the predicted and the reference segmentations, the measure
$S_{\alpha}$ [80] was used:

$S_{\alpha}=\alpha\,S_{O}+(1-\alpha)\,S_{R},$ (25)

where $S_{O}=\mu\,O_{F}+(1-\mu)\,O_{B}$ and
$S_{R}=\sum_{n}w_{n}\,\textit{SSIM}(n)$. Here, $S_{O}$ is the object-level
similarity, $\mu$ is the ratio of the foreground in the reference segmentation
to the image area, and $O_{F}$ and $O_{B}$ are the object-level similarities
between the predicted and reference segmentation in terms of foreground and
background, respectively. The similarity at the region level, $S_{R}$, is
calculated as the weighted foreground ($w_{n}$) region similarity SSIM, where
the latter is the product of three comparison factors including luminance,
contrast, and structure [80]. In our evaluation, we used $\alpha=0.5$ to
calculate $S_{\alpha}$. For more details on the measures, we refer to the
cited works.

##  V Results and Discussion

An ablation study was first performed to evaluate the effectiveness of
integrating DMR blocks in the skip connections of the proposed method. This
study involved comparing the performance of our method with and without DMR.
For this pivotal ablation study, we strategically selected the ISIC 2016 and
ISIC 2017 datasets, which focus specifically on dermatology and skin lesion
analysis. This decision was influenced by two factors. First, skin image
analysis is a field with specific challenges, such as the need for accurate
detection and diagnosis of various skin lesions. As skin maladies manifest in
a variety of ways, these obstacles make skin-related datasets ideal for
evaluating the efficacy of our approach. Additionally, because skin image
analysis is very complicated, adding DMR blocks has many advantages, such as
better information flow, better gradient propagation, a larger receptive
field, and the effective use of fine-grained features. Furthermore, the deep
architecture of our network, which is characterised by a large number of
branches and separable convolution in-depth, enables it to capture intricate
high-frequency details and boundary structures, qualities that are essential
for accurate segmentation of skin lesions. As we investigate our findings more
thoroughly (see Table II), it becomes clear that the incorporation of DMR
blocks substantially improves the overall performance of our proposed
technique.

Dataset | Method | Se | Sp | A | $\textbf{F}_{\mathbf{1}}$ | J
---|---|---|---|---|---|---
ISIC 2016 | Without DMR | 0.9391 | 0.9732 | 0.9635 | 0.9410 | 0.8879
With DMR | 0.9415 | 0.9688 | 0.9738 | 0.9451 | 0.9018
ISIC 2017 | Without DMR | 0.8730 | 0.9311 | 0.9433 | 0.8741 | 0.7955
With DMR | 0.9049 | 0.9561 | 0.9504 | 0.9034 | 0.8356

TABLE II: Effect of the proposed DMR block on the performance of the proposed
network. Best results are in bold.

Method | Se | Sp | A | AUC | $\textbf{F}_{\mathbf{1}}$ | Params (M)
---|---|---|---|---|---|---
SegNet [34] | 0.7949 | 0.9738 | 0.9579 | 0.9720 | 0.8182 | 28.40
Three-Stage FCN [81] | 0.7631 | 0.9820 | 0.9538 | 0.9750 | - | 20.40
Image BTS-DSN [82] | 0.7800 | 0.9806 | 0.9551 | 0.9796 | 0.8208 | 7.80
VessNet [47] | 0.8022 | 0.9810 | 0.9655 | 0.9820 | - | 9
DRIU [54] | 0.7855 | 0.9799 | 0.9552 | 0.9793 | 0.8220 | 7.80
MobileNet-V3 [32] (Lightweight) | 0.8250 | 0.9771 | 0.9371 | 0.9376 | 0.6575 | 2.50
ERFNet [58] (Lightweight) | - | - | 0.9598 | 0.9598 | 0.7652 | 2.06
M2U-Net [36] (Lightweight) | - | - | 0.9630 | 0.9714 | 0.8091 | 0.55
Vessel-Net [44] (Lightweight) | 0.8038 | 0.9802 | 0.9578 | 0.9821 | - | 1.70
MS-NFN [46] (Lightweight) | 0.7844 | 0.9819 | 0.9567 | 0.9807 | - | 0.40
FCN [33] (Lightweight) | 0.8039 | 0.9804 | 0.9576 | 0.9821 | - | 0.20
T-Net [12] (Lightweight) | 0.8262 | 0.9862 | 0.9697 | 0.9867 | 0.8269 | 0.03
Proposed ESDMR-Net (Lightweight) | 0.8320 | 0.9832 | 0.9699 | 0.9853 | 0.8287 | 0.70

TABLE III: Results on the DRIVE dataset. Best results are in bold. Unknown
results are indicated by a dash.

Method | Se | Sp | A | AUC | $\textbf{F}_{\mathbf{1}}$ | Params (M)
---|---|---|---|---|---|---
SegNet [34] | 0.8190 | 0.9735 | 0.9638 | 0.9780 | 0.7981 | 28.40
Three-stage FCN [81] | 0.7641 | 0.9806 | 0.9607 | 0.9776 | - | 20.40
Image BTS-DSN [82] | 0.7900 | 0.9800 | 0.9630 | 0.9840 | 0.7980 | -
DEU-Net [43] | 0.8074 | 0.9821 | 0.9661 | 0.9812 | 0.8037 | -
MobileNet-V3 (Lightweight) [32] | 0.8121 | 0.9601 | 0.9571 | 0.9673 | 0.6837 | 2.50
M2U-Net (Lightweight) [36] | - | - | 0.9703 | 0.9666 | 0.8006 | 0.55
Vessel-Net (Lightweight) [44] | 0.8132 | 0.9814 | 0.966 1 | 0.9860 | - | 1.70
MS-NFN (Lightweight) [46] | 0.7538 | 0.9847 | 0.9637 | 0.9825 | - | -
DPN (Lightweight) [83] | 0.8360 | 0.9701 | 0.9580 | 0.9781 | 0.7790 | 2.40
T-Net (Lightweight) [12] | 0.8323 | 0.9844 | 0.9739 | 0.9889 | 0.8143 | 0.03
Proposed ESDMR-Net (Lightweight) | 0.8380 | 0.9850 | 0.9749 | 0.9881 | 0.8211 | 0.70

TABLE IV: Results on the CHASE dataset. Best results are in bold. Unknown
results are indicated by a dash.

For the segmentation of the retinal vessels, we performed a comprehensive
performance comparison of our ESDMR-Net with 12 other networks previously
evaluated in the literature on the DRIVE dataset. Furthermore, we compared our
network with 11 other networks on the CHASE dataset. This allowed us to assess
the effectiveness of our ESDMR-Net in relation to other state-of-the-art
methods across multiple datasets. In the literature on this segmentation task,
five performance measures are typically reported, namely $Se$, $Sp$, $A$, AUC,
and $F_{1}$, and for comparison networks, we copied their scores for these
measures from the articles cited. The results for DRIVE (Table III), and CHASE
(Table IV) clearly show the superiority of ESDMR-Net for the task, despite the
fact that our network has a smaller number of parameters than almost all the
alternatives. In particular, the proposed network outperformed other
lightweight networks such as MS-NFN [46], FCN [33], Vessel-Net [44], and T-Net
[12]. Visual inspection of the ESDMR-Net segmentation results on DRIVE (Fig.
4), and CHASE (Fig. 5) revealed, not surprisingly, that most segmentation
errors occur in tiny peripheral vessels.

Figure 4: Visual performance of ESDMR-Net on sample images from the DRIVE
dataset. Left to right: original images, corresponding reference images, and
segmentation results of ESDMR-Net, SegNet, and T-Net. Correctly segmented
pixels are highlighted in green (vessels) and black (background) with error
pixels in red (false positives) and blue (false negatives).

Figure 5: Visual performance of ESDMR-Net on sample images from the CHASE
dataset. Left to right: original images, corresponding reference images, and
segmentation results of ESDMR-Net, SegNet, and T-Net. Correctly segmented
pixels are highlighted in green (vessels) and black (background) with error
pixels in red (false positives) and blue (false negatives). TABLE V: Results
on the ISIC 2017 and ISIC 2016 datasets. Best results are in bold. Unknown
results are indicated by a dash.

Method |  | ISIC 2017 |  | ISIC 2016 | Params
---|---|---|---|---|---
|  | Se | Sp | A | $\textbf{F}_{\mathbf{1}}$ | J |  | Se | Sp | A | $\textbf{F}_{\mathbf{1}}$ | J |
U-Net [35] |  | 0.8430 | 0.9341 | 0.9329 | 0.8412 | 0.7569 |  | 0.8728 | 0.9288 | 0.9331 | 0.8824 | 0.8138 | 15.56
UNet++ [84] |  | 0.8713 | 0.9441 | 0.9373 | 0.8635 | 0.7858 |  | 0.8878 | 0.9352 | 0.9388 | 0.8919 | 0.8281 | 18.27
BCDU-Net [85] |  | 0.7646 | 0.9709 | 0.9163 | 0.7811 | 0.7920 |  | 0.7811 | 0.9620 | 0.9178 | 0.8095 | 0.8343 | -
CPFNet [48] |  | - | - | - | - | - |  | 0.9211 | 0.9591 | 0.9509 | 0.9023 | 0.8381 | 11
DAGAN [86] |  | 0.8363 | 0.9725 | 0.9326 | 0.8425 | 0.7594 |  | 0.9228 | 0.9568 | 0.9582 | 0.9085 | 0.8442 | 54
FAT-Net [50] |  | 0.8392 | 0.9725 | 0.9326 | 0.8500 | 0.7653 |  | 0.9259 | 0.9602 | 0.9604 | 0.9159 | 0.8530 | 30
AS-Net [56] |  | 0.8992 | 0.9572 | 0.9466 | 0.8807 | 0.8051 |  | - | - | - | - | - | 24.9
Ms RED (Lightweight) [51] |  | - | - | 0.9410 | 0.8648 | 0.7855 |  | - | - | 0.9642 | 0.9266 | 0.8703 | 1.1
T-Net (Lightweight) [12] |  | 0.8568 | 0.8958 | 0.9201 | 0.8326 | 0.8304 |  | 0.7958 | 0.8751 | 0.8963 | 0.7801 | 0.7880 | 0.03
Attention Res-UNet [37] |  | 0.8831 | 0.9631 | 0.9388 | 0.8789 | 0.8077 |  | 0.8986 | 0.9465 | 0.9438 | 0.9083 | 0.8512 | 45
Swin-Unet [52] |  | 0.8806 | 0.9605 | 0.9476 | 0.8199 | 0.8089 |  | 0.9227 | 0.9579 | 0.9600 | 0.8894 | 0.8760 | 82.3
Proposed ESDMR-Net (Lightweight) |  | 0.9049 | 0.9561 | 0.9504 | 0.9034 | 0.8356 |  | 0.9415 | 0.9688 | 0.9738 | 0.9451 | 0.9018 | 0.7

Figure 6: Visual performance of ESDMR-Net on sample images from the ISIC 2017
dataset. Left to right: original images, corresponding reference images, and
segmentation results of ESDMR-Net, SegNet, and Swin-UNet. Correctly segmented
pixels are highlighted in green (skin) and black (background) with error
pixels in red (false positives) and blue (false negatives).

Figure 7: Visual performance of ESDMR-Net on sample images from the ISIC 2016
dataset. Left to right: original images, corresponding reference images, and
segmentation results of ESDMR-Net, SegNet, and Swin-UNet. Correctly segmented
pixels are highlighted in green (skin) and black (background) with error
pixels in red (false positives) and blue (false negatives).

Method | $\textbf{F}_{\mathbf{1}}$ | $\boldsymbol{\textbf{S}_{\alpha}}$ | $\boldsymbol{\textbf{F}_{\beta}^{\omega}}$ | A | $\boldsymbol{\textbf{E}_{\varphi}^{\max}}$ | MAE | Params (M)
---|---|---|---|---|---|---|---
U-Net++ [87] | 0.7940 | 0.7290 | 0.7850 | 0.8730 | 0.9310 | 0.0220 | 9
SFA [45] | 0.7010 | 0.6070 | 0.6470 | 0.7930 | 0.885 | 0.0420 | 19.80
PraNet [55] | 0.8990 | 0.8490 | 0.8960 | 0.9360 | 0.9790 | 0.0090 | 32.50
T-Net (Lightweight) [12] | 0.9041 | 0.8262 | 0.8892 | 0.9021 | 0.9713 | 0.0105 | 0.03
GLAN (Lightweight) [16] | 0.8993 | 0.8282 | 0.8951 | 0.9113 | 0.9702 | 0.0230 | 0.12
MMS-Net (Lightweight) [15] | 0.9491 | 0.9362 | 0.9051 | 0.9020 | 0.9582 | 0.0240 | 1.3
Proposed ESDMR-Net (Lightweight) | 0.9543 | 0.9478 | 0.9535 | 0.9848 | 0.9851 | 0.0152 | 0.70

TABLE VI: Results on the CVC-ClinicDB dataset. Best results are in bold.

Figure 8: Visual performance of ESDMR-Net on sample images from the CVC-ClinicBD dataset. Left to right: original images, corresponding reference images, and segmentation results of ESDMR-Net, T-Net, and MMS-Net. Correctly segmented pixels are highlighted in green (polyps) and black (background) with error pixels in red (false positives) and blue (false negatives). Method | A | J | $\textbf{F}_{\mathbf{1}}$ | Params (M)
---|---|---|---|---
MobileNet-V3-Small (Lightweight) [32] | 0.9826 | 0.9236 | 0.9576 | 0.47
M2U-Net (Lightweight) [36] | 0.9906 | 0.9615 | 0.9803 | 0.55
ERFNet (Lightweight) [58] | 0.9900 | 0.9590 | 0.9791 | 2.06
T-Net (Lightweight) [12] | 0.9903 | 0.9603 | 0.9787 | 0.03
MKIS-Net (Lightweight) [14] | 0.9908 | 0.9627 | 0.9810 | 0.152
Proposed ESDMR-Net (Lightweight) | 0.9925 | 0.9685 | 0.9840 | 0.7
TABLE VII: Results on the MC dataset. Best results are in bold.

Figure 9: Visual performance of ESDMR-Net on sample images from the MC dataset. Left to right: original images, corresponding reference images, and results of the segmentation of ESDMR-Net, M2UNet and T-Net. Correctly segmented pixels are highlighted in green (chest) and black (background), with error pixels in red (false positives) and blue (false negatives). Method | J | $\textbf{F}_{\mathbf{1}}$ | Params (M)
---|---|---|---
U-Net U-Net [35] | 0.6840 | 0.8190 | 15.56
UNet++ [84] | 0.6830 | 0.8110 | 18.27
BiO-Net [57] | 0.7040 | 0.8240 | 15
Swin-Unet [52] | 0.6377 | 0.7769 | 82.3
UCTransNet [53] | 0.6668 | 0.7987 | 65.6
Proposed ESDMR-Net (Lightweight) | 0.7282 | 0.8424 | 0.7
TABLE VIII: Results on the MoNuSeg dataset. Best results are in bold.

Figure 10: Visual performance of ESDMR-Net on sample images from the MoNuSeg
dataset. Left to right: original images, corresponding reference images, and
segmentation results of ESDMR-Net, Swin-UNet, and T_Net. Correctly segmented
pixels are highlighted in green (cells) and black (background), with error
pixels in red (false positives) and blue (false negatives).

For skin lesion segmentation, we compared ESDMR-Net with 10 other networks on
the ISIC 2017 and ISIC 2016 datasets. Performances are typically reported in
terms of $Se$, $Sp$, $A$, $F_{1}$, and $J$ in the literature. The results
(Table V) show that our method outperformed all other networks in terms of
$Se$, $A$, $F_{1}$, and $J$, despite being a lightweight network. From these
results and illustrative visual examples from the ISIC 2017 dataset (Fig. 6)
and the ISIC 2016 dataset (Fig. 7) we conclude that our method performs well
in a wide range of lesion sizes, shapes, colours and textures.

For polyp segmentation, we compared ESDMR-Net with 6 other networks in the
CVC-ClinicDB dataset. Here, the performance measures included the mean $F_{1}$
score, $S_{\alpha}$, $F_{\beta}^{\omega}$, $A$, $E_{\varphi}^{\max}$, and MAE.
The results (Table VI) again show that ESDMR-Net outperformed other methods in
most performance measures. Visual examples (Fig. 8) illustrate the high
fidelity of the results produced by our network.

For chest X-ray segmentation, we compared ESDMR-Net with four other networks
in the MC dataset. Here, the performance measures included the mean $A$, $J$,
and $F_{1}$. The results (Table VII) demonstrate that ESDMR-Net exceeded other
methods. Furthermore, visual illustrations (Fig. 9) confirm the superior
quality of the segmentations by our network.

Finally, for cell image segmentation, we compared ESDMR-Net with five other
networks on the MoNuSeg dataset. Here, the performance measures reported
included the mean $J$, $F_{1}$, and the training parameters. The results
(Table VIII) show once again that ESDMR-Net outperforms other methods in terms
of most performance measures with visual examples (Fig. 10) that confirm the
high fidelity of the segmentations produced by our network. Collectively, the
results of the considered datasets demonstrate the consistently competitive
performance of ESDMR-Net, demonstrating its robustness in a wide variety of
medical image segmentation tasks.

##  VI Discussion and Conclusions

This paper addresses the challenge of computationally demanding medical image
segmentation approaches by proposing the expand-squeeze dual multiscale
residual network (ESDMR-Net). The network is designed to be highly efficient
on resource-constrained computing hardware, making it well suited for
practical applications. ESDMR-Net gets better at segmentation and capturing
contextual dependencies by using dual-stream information flow and extracting
multiscale features. The ES block in ESDMR-Net plays a crucial role in paying
more attention to under-represented classes, contributing to improved
segmentation performance. The addition of DMR blocks to skip connections also
improves the flow of data across multiple resolutions or scales, allowing the
decoder to access features at different levels of abstraction. This
integration results in more comprehensive feature representations, further
improving the quality of the segmentation. Experiments on five different
medical image segmentation applications demonstrate the effectiveness of
ESDMR-Net. Although it has significantly fewer trainable parameters compared
to other cutting-edge methods, ESDMR-Net outperforms them in terms of
segmentation performance. This has significant implications for the
development of efficient and accurate segmentation models that can be deployed
in resource-constrained settings, ultimately benefiting physicians and
improving patient care.

ESDMR-Net distinguishes itself from other medical image segmentation
approaches by offering a compelling solution that is notable for its
efficiency and lightweight architecture. Conventional methods, such as U-Net
variations and those found by neural architecture search techniques, usually
need a lot of resources, which means that regular hardware cannot use them.
ESDMR-Net performs better than these models due to its low number of
parameters and high computational efficiency, making it a good choice for
deployment in settings with limited memory and processing power. ESDMR-Net is
great at finding the right balance between model complexity and performance.
It solves the important problem of medical image segmentation, which other
designs might find hard to scale down. Unique additions of the expand-squeeze
and dual multiscale residual blocks allow ESDMR-Net to accurately capture
complex features and successfully address the class imbalance. This
demonstrates its superiority over current approaches in handling high-
frequency details and boundary structures in medical pictures.

In terms of limitations and possible future extensions of ESDMR-Net, we note
that while our experiments included a variety of medical imaging datasets,
further investigation is needed to demonstrate the model’s capacity to
generalise across other medical imaging applications. Performance could be
further improved through task-specific optimisations, while interpretability
may be improved by incorporating explainable AI approaches. To prove that
ESDMR-Net is a reliable solution in the complicated field of medical image
segmentation, it is important to look at how it would be used in real life and
compare it to the most advanced models currently available. With continual
improvements and extensions, ESDMR-Net is a potential option to improve the
ability of medical image analysis in settings with limited resources.

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