From WSI-level to Patch-level: Structure Prior Guided Binuclear Cell Fine-grained Detection

• URL: http://arxiv.org/abs/2208.12623v1
• Date: Fri, 26 Aug 2022 12:32:05 GMT
• Title: From WSI-level to Patch-level: Structure Prior Guided Binuclear Cell Fine-grained Detection
• Authors: Baomin Wang, Geng Hu, Dan Chen, Lihua Hu, Cheng Li, Yu An, Guiping Hu, Guang Jia
• Abstract summary: Binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. We propose a two-stage detection method inspired by the structure prior to BC based on deep learning. The coarse detection network is a multi-task detection framework based on circular bounding boxes for cells detection, and central key points for nucleus detection.
• Score: 8.810499770542553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately and quickly binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. However, manual microscopy counting is time-consuming and lacks objectivity. Moreover, with the limitation of staining quality and diversity of morphology features in BC microscopy whole slide images (WSIs), traditional image processing approaches are helpless. To overcome this challenge, we propose a two-stage detection method inspired by the structure prior of BC based on deep learning, which cascades to implement BCs coarse detection at the WSI-level and fine-grained classification in patch-level. The coarse detection network is a multi-task detection framework based on circular bounding boxes for cells detection, and central key points for nucleus detection. The circle representation reduces the degrees of freedom, mitigates the effect of surrounding impurities compared to usual rectangular boxes and can be rotation invariant in WSI. Detecting key points in the nucleus can assist network perception and be used for unsupervised color layer segmentation in later fine-grained classification. The fine classification network consists of a background region suppression module based on color layer mask supervision and a key region selection module based on a transformer due to its global modeling capability. Additionally, an unsupervised and unpaired cytoplasm generator network is firstly proposed to expand the long-tailed distribution dataset. Finally, experiments are performed on BC multicenter datasets. The proposed BC fine detection method outperforms other benchmarks in almost all the evaluation criteria, providing clarification and support for tasks such as cancer screenings.

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