Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Summary

The present protocol describes an efficient multi-organ segmentation method called Swin-PSAxialNet, which has achieved excellent accuracy compared to previous segmentation methods. The key steps of this procedure include dataset collection, environment configuration, data preprocessing, model training and comparison, and ablation experiments.

Abstract

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

Introduction

Contemporary clinical intervention, including the diagnosis of diseases, the formulation of treatment plans, and the tracking of treatment outcomes, relies on the accurate segmentation of medical images¹. However, the complex structural relationships among abdominal organs²make it a challenging task to achieve accurate segmentation of multiple abdominal organs³. Over the past few decades, the flourishing developments in medical imaging and computer vision have presented both new opportunities and challenges in the field of abdominal multi-organ segmentation. Advanced Magnetic Resonance Imaging (MR....

Protocol

The present protocol was approved by the Ethics Committee of Nantong University. It involves the intelligent assessment and research of acquired non-invasive or minimally invasive multimodal data, including human medical images, limb movements, and vascular imaging, utilizing artificial intelligence technology. Figure 3 depicts the overall flowchart of multi-organ segmentation. All the necessary weblinks are provided in the Table of Materials.

Discussion

The segmentation of abdominal organs is a complicated work. Compared to other internal structures of the human body, such as the brain or heart, segmenting abdominal organs seems more challenging because of the low contrast and large shape changes in CT images^27,28. Swin-PSAxialNet is proposed here to solve this difficult problem.

In the data collection step, this study downloaded 200 images from the AMOS2022 official website

Materials

Name	Company	Catalog Number	Comments
AMOS2022 dataset	None	None	Datasets for network training and testing. The weblink is: https://pan.baidu.com/s/1x2ZW5FiZtVap0er55Wk4VQ?pwd=xhpb
ASUS mainframe	ASUS		https://www.asusparts.eu/en/asus-13020-01910200
CUDA version 11.7	NVIDIA		https://developer.nvidia.com/cuda-11-7-0-download-archive
NVIDIA GeForce RTX 3090	NVIDIA		https://www.nvidia.com/en-in/geforce/graphics-cards/30-series/rtx-3090-3090ti/
Paddlepaddle environment	Baidu	None	Environmental preparation for network training. The weblink is: https://www.paddlepaddle.org.cn/
PaddleSeg	Baidu	None	The baseline we use: https://github.com/PaddlePaddle/PaddleSeg