A 3D semantic segmentation network for accurate neuronal soma segmentation  

作　　者：Li Ma Qi Zhong Yezi Wang Xiaoquan Yang Qian Du 

机构地区：[1]School of Mechanical Engineering and Electronic Information,China University of Geosciences Wuhan 430074,P.R.China [2]Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074,P.R.China [3]Department of Electrical and Computer Engineering Mississippi State University,Mississippi State,MS 39762,USA

出　　处：《Journal of Innovative Optical Health Sciences》2025年第1期67-83,共17页创新光学健康科学杂志(英文)

基　　金：supported by the STI2030-Major-Projects(No.2021ZD0200104);the National Natural Science Foundations of China under Grant 61771437.

摘　　要：Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.

关 键 词：Neuronal soma segmentation semantic segmentation network multi-scale feature extraction adaptive weighting fusion 

分 类 号：R318[医药卫生—生物医学工程]