SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
| العنوان: | SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images |
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| المؤلفون: | Tan N. N. Doan, Boram Song, Trinh T. L. Vuong, Kyungeun Kim, Jin T. Kwak |
| المصدر: | IEEE Journal of Biomedical and Health Informatics. 26:3218-3228 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Cell Nucleus, Health Information Management, Histological Techniques, Image Processing, Computer-Assisted, Humans, Health Informatics, Neural Networks, Computer, Electrical and Electronic Engineering, Computer Science Applications |
| الوصف: | Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification. |
| تدمد: | 2168-2208 2168-2194 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05d9170e45dbf58b8891c6a47995b2bc https://doi.org/10.1109/jbhi.2022.3149936 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....05d9170e45dbf58b8891c6a47995b2bc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21682208 21682194 |
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