ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation

TL;DR

This document describes a LaTeX package bundle for preparing manuscripts for Elsevier journals. It introduces two class files, cas-sc.cls for single-column and cas-dc.cls for two-column layouts, along with templates to standardize formatting. A longmktitle option facilitates handling lengthy front matter, addressing front-matter presentation needs. The bundle emphasizes compatibility with Elsevier's electronic submission workflow and provides example materials to illustrate usage and metadata handling.

Abstract

The convolutional neural network-based methods have become more and more popular for medical image segmentation due to their outstanding performance. However, they struggle with capturing long-range dependencies, which are essential for accurately modeling global contextual correlations. Thanks to the ability to model long-range dependencies by expanding the receptive field, the transformer-based methods have gained prominence. Inspired by this, we propose an advanced 2D feature extraction method by combining the convolutional neural network and Transformer architectures. More specifically, we introduce a parallelized encoder structure, where one branch uses ResNet to extract local information from images, while the other branch uses Transformer to extract global information. Furthermore, we integrate pyramid structures into the Transformer to extract global information at varying resolutions, especially in intensive prediction tasks. To efficiently utilize the different information in the parallelized encoder at the decoder stage, we use a channel attention module to merge the features of the encoder and propagate them through skip connections and bottlenecks. Intensive numerical experiments are performed on both aortic vessel tree, cardiac, and multi-organ datasets. By comparing with state-of-the-art medical image segmentation methods, our method is shown with better segmentation accuracy, especially on small organs. The code is publicly available on https://github.com/HongkunSun/ParaTransCNN.