Trans2Unet: Neural fusion for Nuclei Semantic Segmentation

TL;DR

Trans2Unet is a novel two-branch neural network architecture combining Unet and TransUnet, enhanced with a WASP-KC module, achieving improved nuclei segmentation performance on histopathological images.

Contribution

The paper introduces Trans2Unet, a new dual-branch model that fuses CNN and transformer features for better nuclei segmentation, along with an efficient WASP-KC module.

Findings

Outperforms previous models on 2018 Data Science Bowl benchmark.

Effectively handles overlapping nuclei regions.

Improves segmentation accuracy and detail recovery.

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.