A 3D semantic segmentation network for accurate neuronal soma segmentation

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 segme...

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Main Authors: Li Ma, Qi Zhong, Yezi Wang, Xiaoquan Yang, Qian Du
Format: Article
Language:English
Published: World Scientific Publishing 2025-01-01
Series:Journal of Innovative Optical Health Sciences
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Online Access:https://www.worldscientific.com/doi/10.1142/S1793545824500184
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author Li Ma
Qi Zhong
Yezi Wang
Xiaoquan Yang
Qian Du
author_facet Li Ma
Qi Zhong
Yezi Wang
Xiaoquan Yang
Qian Du
author_sort Li Ma
collection DOAJ
description 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.
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spelling doaj-art-b2bb7f6acbf54459b56830e37aee75152025-01-27T05:49:53ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052025-01-01180110.1142/S1793545824500184A 3D semantic segmentation network for accurate neuronal soma segmentationLi Ma0Qi Zhong1Yezi Wang2Xiaoquan Yang3Qian Du4School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, P. R. ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, P. R. ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, P. R. ChinaBritton 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. ChinaDepartment of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USANeuronal 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.https://www.worldscientific.com/doi/10.1142/S1793545824500184Neuronal soma segmentationsemantic segmentation networkmulti-scale feature extractionadaptive weighting fusion
spellingShingle Li Ma
Qi Zhong
Yezi Wang
Xiaoquan Yang
Qian Du
A 3D semantic segmentation network for accurate neuronal soma segmentation
Journal of Innovative Optical Health Sciences
Neuronal soma segmentation
semantic segmentation network
multi-scale feature extraction
adaptive weighting fusion
title A 3D semantic segmentation network for accurate neuronal soma segmentation
title_full A 3D semantic segmentation network for accurate neuronal soma segmentation
title_fullStr A 3D semantic segmentation network for accurate neuronal soma segmentation
title_full_unstemmed A 3D semantic segmentation network for accurate neuronal soma segmentation
title_short A 3D semantic segmentation network for accurate neuronal soma segmentation
title_sort 3d semantic segmentation network for accurate neuronal soma segmentation
topic Neuronal soma segmentation
semantic segmentation network
multi-scale feature extraction
adaptive weighting fusion
url https://www.worldscientific.com/doi/10.1142/S1793545824500184
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