论文标题
使用深度学习的常规组织学图像中的细胞分割和组成
Cellular Segmentation and Composition in Routine Histology Images using Deep Learning
论文作者
论文摘要
结直肠癌丝完全毒素\＆eosin（H \＆E）染色的组织学图像的核核识别和定量对于预后和患者管理至关重要。在计算病理学中，这些任务称为核分割，分类和组成，用于提取有意义的解释性细胞学和建筑特征，用于下游分析。圆锥挑战将自动核分割，分类和组成的任务从最大的公开核数据集-Lizard中置于六种不同类型的核中。在这方面，我们开发了使用悬停网络和ALBRT用于细胞组成的核分割预测的管道。在对初步测试集进行测试时，Hover-NET的PQ为0.58，PQ+为0.58，最后的MPQ+为0.35。为了预测初步测试集中ALBRT的细胞组成，我们达到了总体$ R^2 $得分为0.53，由淋巴细胞为0.84，上皮细胞为0.70，血浆为0.70，用于0.70，嗜酸性粒细胞为.060。
Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred to as nuclear segmentation, classification and composition and are used to extract meaningful interpretable cytological and architectural features for downstream analysis. The CoNIC challenge poses the task of automated nuclei segmentation, classification and composition into six different types of nuclei from the largest publicly known nuclei dataset - Lizard. In this regard, we have developed pipelines for the prediction of nuclei segmentation using HoVer-Net and ALBRT for cellular composition. On testing on the preliminary test set, HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For the prediction of cellular composition with ALBRT on the preliminary test set, we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes, 0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.
