论文标题
在组织病理学图像中弯曲损失正规网络用于核分割
Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images
论文作者
论文摘要
分离重叠的核是组织病理学图像分析中的主要挑战。最近发表的方法在公共数据集上实现了有希望的总体表现；但是，它们在分割重叠核方面的性能受到限制。为了解决这个问题，我们提出了弯曲损失正规化网络以进行核分割。拟议的弯曲损失将高惩罚定义为具有较大曲率的轮廓点，并对曲率较小的轮廓点进行了少量惩罚。最小化弯曲损失可以避免产生包含多个核的轮廓。使用五个定量指标在Monuseg数据集上验证了所提出的方法。在以下指标上，它的表现要优于六种最先进的方法：总jaccard索引，骰子，识别质量和泛光质量。
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.
