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Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image
Detection and segmentation of nuclei are fundamental analysis operations in pathology images, the assessments derived from which serve as the gold standard for cancer diagnosis. Manual segmenting nuclei is expensive and time-consuming. What's more, accurate segmentation detection of nuclei can be challenging due to the large appearance variation, conjoined and overlapping nuclei, and serious degeneration of histological structures. Supervised methods highly rely on massive annotated samples. The existing two unsupervised methods are prone to failure on degenerated samples. This paper proposes a Mutual-Complementing Framework (MCF) for nuclei detection and segmentation in pathology images. Two branches of MCF are trained in the mutual-complementing manner, where the detection branch complements the pseudo mask of the segmentation branch, while the progressive trained segmentation branch complements the missing nucleus templates through calculating the mask residual between the predicted mask and detected result. In the detection branch, two response map fusion strategies and gradient direction based postprocessing are devised to obtain the optimal detection response. Furthermore, the confidence loss combined with the synthetic samples and self-finetuning is adopted to train the segmentation network with only high confidence areas. Extensive experiments demonstrate that MCF achieves comparable performance with only a few nucleus patches as supervision. Especially, MCF possesses good robustness (only dropping by about 6%) on degenerated samples, which are critical and common cases in clinical diagnosis.