Authors:
Mohamed Abdel-Nasser
1
;
2
;
Adel Saleh
3
and
Domenec Puig
1
Affiliations:
1
Computer Engineering and Mathematics Department, University Rovira i Virgili, Tarragona, Spain
;
2
Electrical Engineering Department, Aswan University, Aswan, Egypt
;
3
Gaist Solutions Ltd., U.K.
Keyword(s):
Computational Pathology, Nuclei Segmentation, Whole Slide Imaging, Deep Learning.
Abstract:
In the field of computational pathology, there is an essential need for accurate nuclei segmentation methods for performing different studies, such as cancer grading and cancer subtype classification. The ambiguous boundary between different cell nuclei and the other objects that have a similar appearance beside the overlapping and clumped nuclei may yield noise in the ground truth masks. To improve the segmentation results of cell nuclei in histopathological images, in this paper, we propose a new technique for aggregating the channel maps of semantic segmentation models. This technique is integrated with a self-correction learning mechanism that can handle noisy ground truth. We show that the proposed nuclei segmentation method gives promising results with images of different organs (e.g., breast, bladder, and colon)collected from medical centers that use devices of different manufacturers and stains. Our method reaches the new state-of-the-art. Mainly, we achieve the AJI score of
0.735 on the Multi-Organ Nuclei Segmentation benchmark, which outperforms the previous closest approaches.
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