Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images
| Autor: | Mohamed Abdel-Nasser, Osama A. Omer, Loay Hassan, Adel Saleh, Domenec Puig |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Jaccard index
TK7800-8360 Computer Networks and Communications Computer science Boundary (topology) 02 engineering and technology Stain 030218 nuclear medicine & medical imaging Set (abstract data type) 03 medical and health sciences whole-slide imaging 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Segmentation Electrical and Electronic Engineering Cluster analysis business.industry nuclei segmentation Deep learning deep learning Pattern recognition Choquet integral Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Artificial intelligence Electronics business |
| Zdroj: | Electronics Volume 10 Issue 8 Electronics, Vol 10, Iss 954, p 954 (2021) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics10080954 |
| Popis: | Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clustering. Individual deep learning models are trained based on each stain template, and then, an aggregation function based on the Choquet integral is employed to combine the segmentation masks of the individual models. With a challenging multi-center multi-organ WSIs dataset, the experimental results demonstrate that the proposed method outperforms the state-of-art nuclei segmentation methods with aggregated Jaccard index (AJI) and F1-scores of 73.23% and 89.32%, respectively, while achieving a lower number of parameters. |
| Databáze: | OpenAIRE |
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