Semantic feature refine module for nucleus segmentation using Mask R-CNN

Shan Qin; Rong Zhang; Dayang Yu

doi:10.1117/12.2600801

30 June 2021 Semantic feature refine module for nucleus segmentation using Mask R-CNN

Shan Qin, Rong Zhang, Dayang Yu

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780R (2021) https://doi.org/10.1117/12.2600801
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Nucleus segmentation is a fundamental prerequisite in the digital pathology analysis. However, automated nucleus segmentation is challenging due to clustered arrangement and possible occlusion. Additionally, some nuclei exhibit large variability between images and have fuzzy boundaries. Most of the previous works solve the task through FCN, which requires well-designed post-processing methods to separate instances. In contrast, Mask R-CNN segments objects based on region proposal with no post-processing methods to separate instances, but usually confuses the foreground and background. In this paper, we propose a Semantic Feature Refine Module (SFRM) to enhance its ability to distinguish foreground and background. We first add a semantic segmentation branch to enhance the semantic feature of FPN. Besides, we utilize the feature of semantic segmentation branch to yield an attention pyramid for FPN to enhance its semantic feature further at the same time. Experiments on CoNSeP and PanNuke datasets verify the effectiveness of our method.

Citation Download Citation

Shan Qin, Rong Zhang, and Dayang Yu "Semantic feature refine module for nucleus segmentation using Mask R-CNN", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780R (30 June 2021); https://doi.org/10.1117/12.2600801

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