Academic Journal
A High-Efficiency and High-Accuracy Cellular Segmentation Scheme for Imperfect Cytoarchitecture Images.
| Τίτλος: | A High-Efficiency and High-Accuracy Cellular Segmentation Scheme for Imperfect Cytoarchitecture Images. |
|---|---|
| Συγγραφείς: | Zhang Y; School of Medicine, Jianghan University, Wuhan, China.; Hubei Key Laboratory of Cognitive and Affective Disorders, Jianghan University, Wuhan, China.; Hubei Provincial Demonstration Center for Experimental Medicine Education, School of Medicine, Jianghan University, Wuhan, China., Chen J; School of Medicine, Jianghan University, Wuhan, China.; Hubei Key Laboratory of Cognitive and Affective Disorders, Jianghan University, Wuhan, China.; Hubei Provincial Demonstration Center for Experimental Medicine Education, School of Medicine, Jianghan University, Wuhan, China., Wu Y; Institute of Intelligent Sport and Proactive Health, Department of Health and Physical Education, Jianghan University, Wuhan, China. |
| Πηγή: | Microscopy research and technique [Microsc Res Tech] 2026 May; Vol. 89 (5), pp. 750-758. Date of Electronic Publication: 2025 Dec 29. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9203012 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0029 (Electronic) Linking ISSN: 1059910X NLM ISO Abbreviation: Microsc Res Tech Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : Wiley-Liss, c1992- |
| Ιατρικοί όροι (MeSH): | Brain*/cytology , Brain*/diagnostic imaging , Image Processing, Computer-Assisted*/methods, Animals ; Mice ; Algorithms ; Deep Learning |
| Περίληψη: | Accurate cellular segmentation is essential for cell morphology analysis and disease diagnosis. Traditional manual segmentation is prone to errors, while general segmentation algorithms based on deep learning often fail when dealing with imperfect cytoarchitecture images. This study proposed a high-efficiency and high-accuracy cellular segmentation scheme for such imperfect images. We first enhanced the cell images and then employed the Cellpose algorithm with the Cyto3 pretrained weight module as the foundational model. This scheme requires no additional training, ensuring high efficiency. Experimental results demonstrated a significant improvement in segmentation accuracy, achieving an IoU index of 0.86, ACC index of 0.98, MCC index of 0.91, and Dice of 0.93. When applied to mouse brain images, it successfully quantitatively displayed cell distribution density differences across brain regions. The scheme has great application potential and value in accurate biomedical research, such as quantitative analysis of cell distribution density in different brain regions and cellular localization. (© 2025 Wiley Periodicals LLC.) |
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| Grant Information: | 2023AFA109 Science Fund for Distinguished Young Scholars of Hubei Province |
| Contributed Indexing: | Keywords: cell distribution density; cellular segmentation; image enhancement; imperfect cytoarchitecture images |
| Entry Date(s): | Date Created: 20251229 Date Completed: 20260403 Latest Revision: 20260403 |
| Update Code: | 20260403 |
| DOI: | 10.1002/jemt.70110 |
| PMID: | 41460794 |
| Βάση Δεδομένων: | MEDLINE |
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