Nuclei panoptic segmentation and composition regression with multi-task deep neural networks

• URL: http://arxiv.org/abs/2202.11804v1
• Date: Wed, 23 Feb 2022 22:09:37 GMT
• Title: Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
• Authors: Satoshi Kondo, Satoshi Kasai
• Abstract summary: This report describes our proposed method submitted to the Colon Nuclei Identification and Counting (CoNIC) challenge. Our method employs a multi-task learning framework, which performs a panoptic segmentation task and a regression task. For the panoptic segmentation task, we use encoder-decoder type deep neural networks predicting a direction map in addition to a segmentation map in order to separate neighboring nuclei into different instances.
• Score: 0.12183405753834559
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology. The Colon Nuclei Identification and Counting (CoNIC) Challenge is held to help drive forward research and innovation for automatic nuclei recognition in computational pathology. This report describes our proposed method submitted to the CoNIC challenge. Our method employs a multi-task learning framework, which performs a panoptic segmentation task and a regression task. For the panoptic segmentation task, we use encoder-decoder type deep neural networks predicting a direction map in addition to a segmentation map in order to separate neighboring nuclei into different instances

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