IEEE J Biomed Health Inform. 2022 Jul;26(7):3218-3228. doi: 10.1109/JBHI.2022.3149936. Epub 2022 Jul 1.
Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.
自动核分割和分类是分析和理解细胞特征和功能的关键，支持计算机辅助数字病理学在疾病诊断中的应用。然而，由于不同类型核的大小、强度和形态的固有变化，该任务仍然具有挑战性。在此，我们提出了一种用于同时核分割和分类的自引导有序回归神经网络，该网络可以利用核的内在特征，并在训练过程中关注高度不确定的区域。所提出的网络通过引入距离递减离散化策略将核分割表述为有序回归学习，该策略以一种方式对核进行分层，即形成核的规则形状的内部区域与形成不规则形状的外部区域分开。它还采用了一种自引导训练策略，根据网络自身评估的像素难度自适应地调整与核像素相关的权重。为了评估所提出网络的性能，我们使用了具有 276349 个详尽注释核的大规模多组织数据集。与最近为分割和/或分类开发的几种方法相比，我们展示了所提出的网络在核分割和分类方面均达到了最先进的性能。
