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Abstract:
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Although many automated methods have been proposed for nucleus recognition in histopathology images, most use a multi-stage processing pipeline to categorize nuclei, leading to cumbersome, low-throughput and error-prone assessments. To address this issue, we propose a novel fully convolutional network for single-stage nucleus recognition. Instead of conducting direct pixel-wise classification, we formulate nucleus identification as a deep structured regression model. In addition, by taking into consideration the nucleus distribution in histopathology images, we further introduce an auxiliary task, region of interest (ROI) extraction, to assist and boost the nucleus quantification with weak ROI annotation. The proposed network can be learned in an end-to-end, pixel-to-pixel manner for simultaneous nucleus detection and classification. We have evaluated this network on a pancreatic neuroendocrine tumor Ki67 image dataset, and the experiments demonstrate that our method outperforms recent state-of-the-art approaches. Our method provides a clean, single-stage nucleus recognition pipeline for histopathology image analysis, especially a new perspective for Ki67 image quantification.
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