Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images

被引:7
|
作者
Hassan, Loay [1 ]
Abdel-Nasser, Mohamed [1 ,2 ]
Saleh, Adel [3 ]
Omer, Osama A. [1 ]
Puig, Domenec [2 ]
机构
[1] Aswan Univ, Dept Elect Engn, Aswan 81528, Egypt
[2] Univ Rovira & Virgili, Dept Comp Engn & Math, E-43007 Tarragona, Spain
[3] Gaist Solut Ltd, Skipton BD23 2TZ, England
关键词
nuclei segmentation; whole-slide imaging; deep learning; Choquet integral; DATASET;
D O I
10.3390/electronics10080954
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:17
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