Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

2022-03-01 · Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational pathology applications. In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework. Our solution is part of the Colon Nuclei Identification and Counting (CoNIC) Challenge. We first train a semantic and instance segmentation model separately. Our framework uses as backbone HoverNet and Cascade Mask-RCNN models. We then ensemble the results with a custom Non-Maximum Suppression embedding (NMS). In our framework, the semantic model computes a class prediction for the cells whilst the instance model provides a refined segmentation. We demonstrate, through our experimental results, that our model outperforms the provided baselines by a large margin.

📄 PDF Abstract BibTeX arXiv:2203.00157

Code (1)

lihaoliu-cambridge/simultaneous_semantic_and_instance_segmentation 공식 구현 pytorch

Tasks

Instance Segmentation Nuclear Segmentation Segmentation Semantic Segmentation

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Diagnostic Image Generation Segmentation

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Classification Explainable Models Nuclear Segmentation Segmentation

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Classification Instance Segmentation Segmentation Semantic Segmentation