دورية أكاديمية
Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue.
| العنوان: | Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue. |
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| المؤلفون: | Fakhrzadeh A; Iranian Research Institute for Information Science and Technology, Information Technology Department, Tehran, Iran., Karimian P; Amirkabir University of Technology (Tehran Polytechnic), Industrial Engineering and Management Systems Department, Tehran, Iran., Meyari M; Amirkabir University of Technology (Tehran Polytechnic), Industrial Engineering and Management Systems Department, Tehran, Iran., Luengo Hendriks CL; Deepcell, Inc., Menlo Park, California, United States., Holm L; Swedish University of Agricultural Sciences, Department of Anatomy, Physiology, and Biochemistry, Uppsala, Sweden., Sonne C; Aarhus University, Arctic Research Centre, Department of Ecoscience, Roskilde, Denmark., Dietz R; Aarhus University, Arctic Research Centre, Department of Ecoscience, Roskilde, Denmark., Spörndly-Nees E; National Veterinary Institute, Department of pathology and wildlife Diseases, Uppsala, Sweden. |
| المصدر: | Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2023 Feb; Vol. 10 (Suppl 1), pp. S17501. Date of Electronic Publication: 2023 May 03. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Society of Photo-Optical Instrumentation Engineers Country of Publication: United States NLM ID: 101643461 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2329-4302 (Print) Linking ISSN: 23294302 NLM ISO Abbreviation: J Med Imaging (Bellingham) Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Bellingham, Wash. : Society of Photo-Optical Instrumentation Engineers |
| مستخلص: | Purpose: There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. We propose an automated method to process histology images of testicular tissue. Approach: Testicular tissue consists of seminiferous tubules. Segmenting the epithelial layer of the seminiferous tubule is a prerequisite for developing automated methods to detect abnormalities in tissue. We suggest an encoder-decoder fully connected convolutional neural network model to segment the epithelial layer of the seminiferous tubules in histological images. The ResNet-34 is used in the feature encoder module, and the squeeze and excitation attention block is integrated into the encoding module improving the segmentation and localization of epithelium. Results: We applied the proposed method for the two-class problem, where the epithelial layer of the tubule is the target class. The F -score and Intersection over Union of the proposed method are 0.85 and 0.92. Although the proposed method is trained on a limited training set, it performs well on an independent dataset and outperforms other state-of-the-art methods. Conclusion: The pretrained ResNet-34 in the encoder and attention block suggested in the decoder result in better segmentation and generalization. The proposed method can be applied to testicular tissue images from any mammalian species and can be used as the first part of a fully automated testicular tissue processing pipeline. The dataset and codes are publicly available on GitHub. (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).) |
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| فهرسة مساهمة: | Keywords: deep learning; histological image; segmentation; seminiferous tubules |
| تواريخ الأحداث: | Date Created: 20230508 Latest Revision: 20230509 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC10154426 |
| DOI: | 10.1117/1.JMI.10.S1.S17501 |
| PMID: | 37153721 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2329-4302 |
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| DOI: | 10.1117/1.JMI.10.S1.S17501 |