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Separating overlapped nuclei is a major challenge in histopathology imageanalysis. Recently published approaches have achieved promising overallperformance on public datasets; however, their performance in segmentingoverlapped nuclei are limited. To address the issue, we propose the bendingloss regularized network for nuclei segmentation. The proposed bending lossdefines high penalties to contour points with large curvatures, and appliessmall penalties to contour points with small curvature. Minimizing the bendingloss can avoid generating contours that encompass multiple nuclei. The proposedapproach is validated on the MoNuSeg dataset using five quantitative metrics.It outperforms six state-of-the-art approaches on the following metrics:Aggregate Jaccard Index, Dice, Recognition Quality, and Pan-optic Quality.

Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images