  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.
