PAIP 2019: Liver cancer segmentation challenge

•The Pathology Artificial Intelligence Platform (PAIP) is a free research support platform.•The PAIP Liver Cancer Segmentation Challenge, Task 1 Liver Cancer Segmentation and Task 2 Viable Tumor Burden Estimation.•Most top-performing methods used deep convolutional networks (such as U-Net) with mult...

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Published in:	Medical image analysis Vol. 67; p. 101854
Main Authors:	Kim, Yoo Jung, Jang, Hyungjoon, Lee, Kyoungbun, Park, Seongkeun, Min, Sung-Gyu, Hong, Choyeon, Park, Jeong Hwan, Lee, Kanggeun, Kim, Jisoo, Hong, Wonjae, Jung, Hyun, Liu, Yanling, Rajkumar, Haran, Khened, Mahendra, Krishnamurthi, Ganapathy, Yang, Sen, Wang, Xiyue, Han, Chang Hee, Kwak, Jin Tae, Ma, Jianqiang, Tang, Zhe, Marami, Bahram, Zeineh, Jack, Zhao, Zixu, Heng, Pheng-Ann, Schmitz, Rüdiger, Madesta, Frederic, Rösch, Thomas, Werner, Rene, Tian, Jie, Puybareau, Elodie, Bovio, Matteo, Zhang, Xiufeng, Zhu, Yifeng, Chun, Se Young, Jeong, Won-Ki, Park, Peom, Choi, Jinwook
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01-01-2021 Elsevier BV
Subjects:	Algorithms Artificial intelligence Automation Cancer Challenge Datasets Digital pathology Image analysis Image processing Image segmentation Liver Liver cancer Medical imaging Pathology Performance evaluation Segmentation Teams Tumor burden Tumors Tumor burden Liver cancer Digital pathology Segmentation 41A10 65D05 65D17 41A05 Challenge
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Summary:	•The Pathology Artificial Intelligence Platform (PAIP) is a free research support platform.•The PAIP Liver Cancer Segmentation Challenge, Task 1 Liver Cancer Segmentation and Task 2 Viable Tumor Burden Estimation.•Most top-performing methods used deep convolutional networks (such as U-Net) with multi-scale ensemble methods. [Display omitted] Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team’s algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1361-8415 1361-8423
DOI:	10.1016/j.media.2020.101854