Abstract
Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings |
Editors | Xiaohuan Cao, Xi Ouyang, Xuanang Xu, Islem Rekik, Zhiming Cui |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 457-466 |
Number of pages | 10 |
ISBN (Print) | 9783031456756 |
DOIs | |
Publication status | Published - 15 Oct 2023 |
Event | 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 8 Oct 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14349 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 |
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Country/Territory | Canada |
City | Vancouver |
Period | 8/10/23 → 8/10/23 |
Bibliographical note
Publisher Copyright:© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Attention
- Brain
- Cell Segmentation
- Deep-Learning
- EfficientNet
- Histological Images
- Neuroanatomy
- Nissl Staining
- U-Net