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

TL;DR

This paper introduces a two-stage deep learning method for binuclear cell detection in microscopy images, combining structure prior guidance with innovative modules for coarse and fine classification, improving accuracy and robustness.

Contribution

The paper presents a novel two-stage detection framework utilizing structure prior, multi-task detection, and an unpaired cytoplasm generator for enhanced binuclear cell detection.

Findings

Outperforms benchmark methods in most evaluation metrics.

Effective in handling diverse and challenging microscopy images.

Provides a robust tool for cancer screening applications.

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.