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15 February 2021 Distance ordinal regression loss for an improved nuclei segmentation
Tan Nhu Nhat Doan, Chang Hee Han, Jin Tae Kwak
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Proceedings Volume 11603, Medical Imaging 2021: Digital Pathology; 1160315 (2021) https://doi.org/10.1117/12.2581016
Event: SPIE Medical Imaging, 2021, Online Only
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Abstract
In digital pathology, nuclei segmentation still remains a challenging task due to the high heterogeneity and variability in the characteristics of nuclei, in particular, the clustered and overlapping nuclei. We propose a distance ordinal regression loss for an improved nuclei instance segmentation in digitized tissue specimen images. A convolutional neural network with two decoder branches is built. The first decoder branch conducts the nuclear pixel prediction and the second branch predicts the distance to the nuclear center, which is utilized to identify the nuclear boundary and to separate out overlapping nuclei. Adopting a distance-decreasing discretization strategy, we recast the problem of the distance prediction as an ordinal regression problem. To evaluate the proposed method, we conduct experiments on multiple independent multitissue histology image datasets. The experimental results on the multi-tissue datasets demonstrate the effectiveness of the proposed model.
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Tan Nhu Nhat Doan, Chang Hee Han, and Jin Tae Kwak "Distance ordinal regression loss for an improved nuclei segmentation", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 1160315 (15 February 2021); https://doi.org/10.1117/12.2581016
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KEYWORDS
Image segmentation
Convolutional neural networks
Data modeling
Pathology
Tissues
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