# Trans2Unet: Neural fusion for Nuclei Semantic Segmentation

Dinh-Phu Tran  Department of Automation Engineering
School of Electrical and Electronic Engineering
Hanoi University of Science and Technology
Hanoi, Vietnam
<EMAIL_ADDRESS>Quoc-Anh Nguyen  Department of Automation
Engineering
School of Electrical and Electronic Engineering
Hanoi University of Science and Technology
Hanoi, Vietnam
<EMAIL_ADDRESS>Van-Truong Pham  Department of Automation
Engineering
School of Electrical and Electronic Engineering
Hanoi University of Science and Technology
Hanoi, Vietnam
<EMAIL_ADDRESS>Thi-Thao Tran∗ Department of Automation
Engineering
School of Electrical and Electronic Engineering
Hanoi University of Science and Technology
Hanoi, Vietnam
<EMAIL_ADDRESS>
###### Abstract

Nuclei segmentation, despite its fundamental role in histopathological image
analysis, is still a challenge work. The main challenge of this task is the
existence of overlapping areas, which makes separating independent nuclei more
complicated. In this paper, we propose a new two-branch architecture by
combining the Unet and TransUnet networks for nuclei segmentation task. In the
proposed architecture, namely Trans2Unet, the input image is first sent into
the Unet branch whose the last convolution layer is removed. This branch makes
the network combine features from different spatial regions of the input image
and localizes more precisely the regions of interest. The input image is also
fed into the second branch. In the second branch, which is called TransUnet
branch, the input image will be divided into patches of images. With Vision
transformer (ViT) in architecture, TransUnet can serve as a powerful encoder
for medical image segmentation tasks and enhance image details by recovering
localized spatial information. To boost up Trans2Unet efficiency and
performance, we proposed to infuse TransUnet with a computational-efficient
variation called ”Waterfall” Atrous Spatial Pooling with Skip Connection
(WASP-KC) module, which is inspired by the ”Waterfall” Atrous Spatial Pooling
(WASP) module. Experiment results on the 2018 Data Science Bowl benchmark show
the effectiveness and performance of the proposed architecture while compared
with previous segmentation models.

###### Index Terms:

Unet, TransUnet, Vision Transformer, WASP, Image Medical Segmentation, Nuclei
segmentation

##  I Introduction

Cell nuclei segmentation has been a crucial problem that attracts interest
because of its practical applications in the diagnosis of cancer. In general,
this task is similar to natural image segmentation, which involves the process
of extracting desired objects from a nuclei image (image 2D or 3D), and can be
done manually, semi-automatically, or full-automatically [5][6][7]. Recently,
many deep learning models with high accuracy have been used for nuclei
segmentation [8]. In 2015, Unet featured an encoder-decoder architecture
combined with skip-connections to retain important features has showed
outstanding results for segmentation tasks, especially medical images.
Although having been powerful network architectures, Unet and other CNN
networks in general still have limitations in reproducing straightforward
long-range interrelationships resulted from the intrinsic locality of
convolution operations. Unlike the CNN-based networks, the models based on
Transformers have global computing features. In [2], TransUnet was proposed to
solve that problem by employing a hybrid CNN-Transformer approach to enhance
both elaborate high-resolution spatial information from feature maps of CNN
and the global context, which is encoded by Transformers. Although Transformer
has gained popularity in Computer Vision due to global features, the lack of
low-level details makes local feature information extraction insufficient
[10].
To take full advantages of Unet and TransUnet, in this study, we propose to
combine these two architectures to obtain a more powerful architecture. The
proposed architecture named as Trans2Unet includes two main branches. One
branch sends the input image through the Unet network, the other branch sends
the input image through the TransUnet network. Finally, the outputs of these
branches are concatenated to recreate feature maps of the input image, thereby
improving the predictive ability of the model. Furthermore, instead of using
the original TransUnet architecture, we added the WASP-KC module to leverage
the progressive extraction of a larger field-of-view (FOV) block from cascade
methods.
Our main contributions can be briefed as follows:

  * •

Introduce a new, more robust, and efficient architecture using Unet and
TransUnet networks.

  * •

Add a WASP-KC block for the TransUnet model after the CNN block.

Through hands-on experiments on the 2018 Data Science Bowl challenge dataset,
the results showed that the proposed network has achieved fairly good accuracy
compared to other SOTA architectures on this same data set. Specifically, we
have obtained 2 parameters DSC and IoU with values of 0.9225 and 0.8613.
The following is the organization of this paper: Firstly, the related work is
described in section II. Section III introduces our proposed model.
Experimental results on the 2018 Data Science Bowl challenge dataset is
obtained in Section IV. Finally, the summaries, limitations, and further work
are described in Section V.

##  II Related Work

###  II-A (Unet)

Unet was first proposed in 2015, known as an effective Convolutional Network
for Biomedical Image Segmentation. Unet architecture contains two paths, an
encoder and a decoder. The encoder path is the downsampling part, each block
has a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride
2 [9]. The decoder path is the upsampling part for reconstructing the high-
resolution feature map of the image. In particular, Unet uses skip connections
to preserve spatial information because during downsampling at the encoder
path, spatial information of the input image is lost, causing architecture
accuracy degradation.

###  II-B (ViT)

In terms of natural language processing tasks (NLP), it has been known that
Transformer architecture is one of the key criteria. However, when it comes to
Computer Vision tasks, this model still has many limitations [3]. Vision
transformer (ViT) is a pioneering model that adapts the transformer model to
Computer Vision (CV) tasks by embedding input images into a series of visual
tokens and modeling the global dependencies among this sequence with a group
of transformer blocks. ViT simply processes the input image as a 1D sequence
which leads to a lack of inductive bias in modeling local visual structures
[11]. Recently, ViT has achieved highly competitive accuracy benchmarks in a
variety of applications: image classification, object detection, and semantic
image segmentation. Taking inspiration from processing input images as a
sequence, the Vision transformer is a combination of the Transformer
architecture part and MLP (Multilayer Perceptron) blocks [1]. The Transformer
encoder of ViT includes Multi-Head Self Attention Layer (MHSA), Multi-Layer
Perceptrons (MLP) Layer, and Layer Norm (LN) [12]. MHSA is the key component
of the Transformer block. It is achieved after repeating single-head self-
attention (SHSA) for n times, where n is the quantity of heads. MHSA is
intended to reproduce long-range structural data from the images [13].

###  II-C (TransUnet)

TransUnet can also be considered an upgraded version of Unet. TransUnet is the
first architecture to use transformers for tasks related to Computer Vision
and it has opened up new research directions with the successful application
of transformers to image tasks. The big difference between TransUnet and Unet
lies in the Encoder Path. There is a fairly detailed description of the
TransUnet Encoder path architecture in Fig. 2. It includes CNN Block (in the
study [2] the author used the backbone as ResNet50) and Vision TransFormer
(ViT). The encoder which applies the transformer in ViT comprises successive
layers of multiheaded self-attention (MHSA), and MPL blocks. Instead of using
BatchNorm (BN), the transformer block uses LayerNorm (LN) before each one, and
after each block, residual connections are put [16][17].

###  II-D DeepLabv3+

In research [14], the Atrous Spatial Pyramid Pooling module (ASPP) was
proposed to be integrated with the encoder-decoder structure and this research
showed better improvements to the boundaries of segmented objects in the input
images. The special structure of ASPP assembled dilated convolutions in four
parallel branches with distinct levels. Ultimately, being combined by fast
bilinear interpolation with an additional factor of eight, the resulting
feature maps were recovered to the original solution [3]. The DeepLabv3+
significantly improved over the previous version in terms of accuracy.

###  II-E Waterfall Atrous Spatial Pooling (WASP)

The WASP is a highly efficient architecture for semantic segmentation. It
leverages progressive filtering in a cascading architecture while preserving
multiscale fields-of-view (FOV) in comparison with spatial pyramid
configurations. According to the study in [3], WASP when combined with the
Resnet backbone will provide a robust architecture and obtain potential
results for segmentation problems. Furthermore, this variation has effective
computation, which is an Atrous Spatial Pooling (ASP) class variant in the
DeepLabv3+ architecture. [15] demonstrated the great improvement of the WASP
module in terms of computation time in training progress and decreasing
parameters compared to the original ASPP module.

##  III Methodology

###  III-A Waterfall Atrous Spatial Pooling with Skip Connection (WASP-KC)
Module

Figure 1: Waterfall Atrous Spatial Pooling (WASP-KC) module

The WASP-KC module, shown in Fig.1, is inspired by the WASP module. The WASP-
KC involves four units of a large-FOV that merges together and create a
waterfall shape to give output.Besides multiscale approaches [26][23], this
module is also inspired by the cascade configuration [3][14], as well as by
the parallel structures of ASPP [24] and Res2Net modules [25].The WASP module
helps to reduce parameters and memory required, which leads to less expensive
computation,the main limitation of Atrous Convolutions [3][15]. According to
the experiments performed by the authors in [3], the WASP module successfully
reduced 20.69% of the parameters and also increases the model’s performance by
2% (mIoU) using WASPnet network built on this module compared to the Res2Net-
Seg or ASPP modules. In this research, we have replaced the WASP block with
the WASP-KC block by Dense connections, which are inspired by the DenseNet
model. In this technique, every single layer takes all previous layers’ output
as input, and its feature map will be brought to deeper layers, which means
that each layer receives the whole information from the previous ones. This
will ensure feature reusability since feature maps of prior layers are held
and added altogether which helps input image data be well kept without any
loss. This is a significant modification that makes WASP can function more
robustly. The WASP-KC block is added right after the CNN module (ResNet-50
backbone is used) to improve the performance and efficiency of the proposed
model.

Figure 2: Architecture of TransUnet after adding WASP-KC module

Figure 3: Architecture of Trans2Unet

###  III-B Model architecture

Aiming at developing a new deep learning architecture for nuclei cell image
segmentation, this study proposed the Trans2Unet which combines Unet and
TransUnet branches. First, to increase the efficiency of the TransUnet branch,
we used an additional the WASP-KC block as shown in Fig. 2. The WASP-KC block
consists of four convolution units. Each unit includes of three blocks, the
first block uses convolution 3x3, followed by two blocks applying convolution
1x1. The 3x3 convolution blocks share information horizontally, through which
the information will be used in all units of the module. In addition, skip
connection is used in each unit to use the features of the previous layers.
This adjustment has improved performance significantly compared to the WASP
module. The output of the module is the sum of these 4 units and output of the
global average pooling block, and will also be the input of the ViT network.

Fig. 3 shows the general structure of the proposed Trans2Unet that includes
the Unet branch and the proposed TransUnet+WASP-KC branch. After the input
image has been forwarded through these two branches, the outputs of the two
branches will be concatenated together. And finally, after aggregating the
output of the two branches above, we continue to forward through a Convolution
block before making the predicted output. This is a fairly new and simple
combination, but it improves performance much better than just using Unet or
TransUnet as usual.

###  III-C Loss function

The loss function, also known as the cost function, is an equation
representing the relationship between q (which is the model’s predicted
result) and p (which is the actual value). Our task is to minimize the value
of this equation. The loss function is used to optimize models and this is
also one of the parameters to evaluate the quality of the model. Tasks related
to image segmentation have many loss functions applied such as Binary Cross-
Entropy (BCE), Dice loss, …
The binary cross-entropy (BCE) loss function calculates the difference between
two probability distributions, they are the actual probability distribution p
and the predicted probability distribution q. It is commonly used for object
classification tasks, and in image segmentation tasks as it is classification
on pixels. This should be used for balanced datasets. BCE loss is represented
by the following equation:

$L(p,q)=-y\log(q)-(1-p)\log(1-q)$ (1)

Where p represents the ground truth label, q represents the predicted value of
the Trans2Unet model. The value of (1) reflects the difference between the
actual value and the value predicted from the model.

Dice Loss is a loss function that is popularly used in tasks relating to
arcing image segmentation or medical image segmentation. . The value of this
loss function measures the difference between the ground truth and the
predicted value. Dice Loss is represented by the following equation:

$DL(p,q)=1-\frac{2pq+1}{p+q+1}$ (2)

Mathematical notations $(p,q)$ have the meaning similar to Binary Cross-
Entropy part.

###  III-D Evaluation Metrics

Currently, The Dice Similarity Score (DSC) and Jaccard Index or Intersection
over Union (IoU) are the most popular indexes for evaluating models in medical
image segmentation [18][19][20]. In this research, we also use these two
parameters to make a fair comparison with other models on the 2018 Data
Science Bowl challenge dataset.
The DCS and IoU are defined by the following mathematical expressions [21]:

$DSC=\frac{2TP}{2TP+FP+FN}$ (3)

Where: TP, FP, FN, TN are the number of true positive, false positive, false
negative, and negative predictions. In addition, in the study[22], there are
other evaluation metrics for task image segmentation such as Precision,
Accuracy, Volumetric Similarity, …

$IoU=\frac{TP}{TP+FP+FN}$ (4)

##  IV EXPERIMENTAL RESULTS

###  IV-A Dataset

To properly assess the performance evaluation of Trans2Unet model, we used the
public biomedical image dataset - the 2018 Data Science Bowl challenge dataset
and GlaS dataset. The 2018 Data Science Bowl challenge dataset contains the
original images, along with their masks (or ground-truth). There are 670
images in total, we splitted this dataset into the ratio of 80% - 10% - 10%
corresponding to the training set - validation set - test set. Some of State-
of-the-art models tested on 2018 Data Science Bowl such as SSFormer-L,
MSRFNet, DoubleUnet, Unet++… have achieved remarkable results. Following this
dataset with the same split ratio, through trials and errors, we are confident
that 670 images are enough for proposed model to perform robustly. GlaS
dataset contains 165 microscopic images and the corresponding target mask
annotations. In this work we split GlaS dataset into 85 training images and 80
testing images.

###  IV-B Implementation detail

We have implemented this entire proposed architecture with the Pytorch
framework and conducted experiments with NVIDIA K80 GPUs. The Adam
optimization function has been deprecated, with the initial learning rate (LR)
set to 0.0003, and we also used a dropout regularization with p = 0.2. After
three epochs with no improvement, the new learning rate is calculated by
multiplying the current learning rate by a factor, which is a small value
enough to reduce current learning rate and global minimum is still reached.
All images in the 2018 Data Science Bowl challenge and GlaS dataset will be
resized to 256 x 256 resolution. Batch size used is 10 and the number of
epochs to train our model is 300.

###  IV-C Evaluation

In this research, we referred our model to some of the models that achieved
remarkable results in the 2018 Data Science Bowl challenge and GlaS dataset to
objectively review the effectiveness of this approach. In table 1, the scores
reported by previous algorithms in terms of average values of the Dice
Similarity Score (DSC) and IoU indexes are compared with those by the proposed
approach. The table shows that our new approach gives the results that are
confirmed to be good on the 2018 Data Science Bowl challenge dataset with the
values of DSC - IoU are 0.9225, and 0.8613 respectively (when we fused Unet
with TransUnet).

As described in Table 1, the number of Trans2Unet parameters are up to 110M,
which is a disadvantage that needs improvement in upcoming research, whereas
those of SSFormer-L model are 66.2M. The explanation for huge size of our
proposed network is due to ViT model used in TransUnet branch. As [1], there
are 3 variants of Vit consisting of ViT-Base (86M parameters), ViT-Large (307M
parameters), and ViT-Huge(632M parameters). Considering these sizes, we
decided to use ViT-Base model in our network.

Method | Dice Coefficient | Mean IoU | Parameters (M)
---|---|---|---
SSFormer-L [27] | 0.9230 | 0.8614 | 66.2
TransUnet | 0.9027 | 0.8413 | 105.9
MSRF-Net [28] | 0.9224 | 0.8534 | 18.38
FANet [29] | 0.9176 | 0.8569 | 5.76
DoubleUNet [30] | 0.9133 | 0.8407 | 29.29
Trans2Unet (Ours) | 0.9225 | 0.8613 | 110
TABLE I: Performances comparison of various model on the 2018 Data Science
Bowl challenge dataset.

Although our results are still modest compared to other current SOTA
architectures on this dataset, we believe that with this approach, the
architecture will be improved in the future.

To show the improvement of the Trans2Unet model integrating with the WASP-KC
module more clearly, IoU and Dice metrics of this model were compared with
those of the original TransUnet as well as the Trans2Unet model integrating
with the original WASP, and all experiments were tested on the same device.
The results reported in table II show that IoU and Dice metrics of our
proposed model are second to none, specifically, this model has the IoU and
Dice metrics are 86.13% and 92.25%, respectively.

Method | Dice Coefficient | Mean IoU
---|---|---
TransUnet | 0.9027 | 0.8413
Trans2Unet + WASP | 0.9150 | 0.8499
Trans2Unet + WASP-KC | 0.9225 | 0.8613
TABLE II: Performances comparison of Trans2Unet with WASP-KC module and its
baseline models.

As can be seen from Table 3, the results show that proposed network Trans2Unet
also obtained great performance on GlaS dataset with Dice Coefficient of
89.94% and Mean IoU of 82.54%.

Method | Dice Coefficient | Mean IoU
---|---|---
FCN[32] | 0.6661 | 0.5058
Unet[9] | 0.7778 | 0.6534
Res-Unet[33] | 0.7883 | 0.6595
Axial Attention Unet[34] | 0.7630 | 0.6303
KiU-Net[35] | 0.8325 | 0.7278
Trans2Unet (Ours) | 0.8984 | 0.8254
TABLE III: Comparisons with various method on GlaS Dataset.

###  IV-D Results

To demonstrate the performance of the new architecture on the 2018 Data
Science Bowl challenge dataset, we show the learning curves in Fig. 4. As
shown in this figure, the model loss and scores including the Dice (DSC), and
IoU converge after 100 epochs and stay stable. For qualitative assessment, we
also show some representative segmentation results of the test set of this
dataset in Fig. 5. It is obvious in Fig.5, the predictions by the proposed
approach are in good agreement with those by ground truths.

Figure 4: Training curves on the 2018 Data Science Bowl challenge dataset

Figure 5: Some representative segmentation results of Trans2Unet on Nuclei
images from 2018 Data Science Bowl challenge dataset

##  V Conclusion

In this study, we have introduced a new architecture, which is a combination
of two other deep learning networks, Unet and TransUnet, for nuclei image
segmentation. Furthermore, to approach leverages the progressive extraction of
larger fields-of-view (FOV) from cascade methods, we integrated WASP-KC (WASP
module with Skip Connections) module into the TransUnet architecture. Through
experiments on the 2018 Data Science Bowl challenge dataset, we show that our
proposed model has achieved quite good results expressed through DSC or IoU
scores. By combining the Unet with the TransUnet architecture, the model can
maintain local features of CNN and take advantage of global features in
Transformers for more robust segmentation. We believe that this structure can
be a good approach to improve the efficiency of models not only for nuclei
cell but also for general tasks of image segmentation.

## Acknowledgment

This research is funded by the Hanoi University of Science and Technology
(HUST) under project number T2021- PC-005.

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