论文标题
全扫描组织病理学图像中肺癌分割的深度学习方法 - ACDC@lunghp挑战2019
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
论文作者
论文摘要
肺癌在病理幻灯片中的准确分割是改善患者护理的关键步骤。我们提出了针对肺癌自动诊断的不同计算机辅助诊断（CADS）方法，提出了ACDC@lungHP（全扫描肺部组织病理学中的自动癌症检测和分类）挑战。 ACDC@lunghp 2019使用150次培训图像和50张来自200名患者的测试图像的注释数据集，重点是整个幻灯片成像（WSI）中癌组织（WSI）中的分割（像素的检测）。本文回顾了这一挑战，并总结了提交的十大肺癌分割方法。使用假阳性速率，假负率和骰子系数（DC）评估所有方法。 DC从0.7354 $ \ pm $ 0.1149到0.8372 $ \ pm $ 0.0858。最佳方法的DC接近观察者协议（0.8398 $ \ pm $ 0.0890）。所有方法均基于深度学习，并将其分为两组：多模型方法和单个模型方法。通常，多模型方法明显好（$ \ textit {p} $ <$ 0.01 $），平均直流分别为0.7966和0.7544。基于深度学习的方法可能有可能帮助病理学家找到可疑区域，以进一步分析WSI中的肺癌。
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using the false positive rate, false negative rate, and DICE coefficient (DC). The DC ranged from 0.7354$\pm$0.1149 to 0.8372$\pm$0.0858. The DC of the best method was close to the inter-observer agreement (0.8398$\pm$0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better ($\textit{p}$<$0.01$) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.
