Abstract
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 0.7354 ±0.1149 to 0.8372 ±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 ±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 (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.
Highlights
L UNG cancer is the top cause of cancer-related death in the world
To further explore the potential application of deep learning (DL) on whole slide imaging (WSI) for lung cancer diagnosis, we proposed the ACDC@LungHP challenge which is the first challenge at addressing lung cancer detection and classification using WSI, to our best knowledge [45]
The current stage of the challenge focused on lung cancer segmentation. 200 slides were used for this challenge, and methods from the top 10 teams were selected for comparison
Summary
L UNG cancer is the top cause of cancer-related death in the world. According to the 2009-2013 SEER (Surveillance, Epidemiology, and End Results) database, the 5-year survival rate of lung cancer patients is approximately 18% [1]. The CAMELYON16 was the first challenge to offer WSIs a large number of annotations, which is essential for training deeper CNNs. With the breakthrough of DL methods in medical image analysis and increasing of available public WSIs for developing a specific CNN, we believe that the CNN could be leveraged to give pathologists more reliable objective results or even help pathologists to improve the cancer diagnostic level. The recent research [49] suggested that image features automatically extracted from WSIs can predict the prognosis of lung cancer patients and thereby contribute to precision oncology by machine learning classifiers.
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