Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images - The ACDC@LungHP Challenge 2019

Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Hong Zhao, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu Cheng ChenChing Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Li Yu, Zhihong Liu, Daiqiang Li, Peter J. Schuffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


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 metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354pm0.1149 to 0.8372pm0.0858. The DC of the best method was close to the inter-observer agreement (0.8398pm0.0890). 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 (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.

Original languageEnglish
Article number9265237
Pages (from-to)429-440
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
StatePublished - Feb 2021
Externally publishedYes


  • Artificial intelligence
  • convolutional neural networks
  • deep learning
  • lung cancer


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