基于弯曲损失正则化网络的细胞核分割
Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images
摘要：
分离重叠的细胞核是组织病理学图像分析中的主要挑战。最近发表的方法已经取得了可喜的整体性能在公共数据集，但是，它们的性能分割重叠的细胞核是有限的。为了解决这个问题，我们提出了弯曲损失正则化网络的细胞核分割。所提出的弯曲损失定义了高的惩罚轮廓点与大曲率，并适用于小曲率的轮廓点的小惩罚。最小化弯曲损失可以避免生成包含多个核的轮廓。该方法在MoNuSeg数据集上使用五个定量指标进行了验证。它在以下指标上优于六种最先进的方法：聚合Jaccard指数，骰子，识别质量和泛光学质量。
英文摘要：
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on public datasets; however, their performance in segmenting overlapped nuclei are limited. To address the issue, we propose the bending loss regularized network for nuclei segmentation. The proposed bending loss defines high penalties to contour points with large curvatures, and applies small penalties to contour points with small curvature. Minimizing the bending loss can avoid generating contours that encompass multiple nuclei. The proposed approach 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.
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