PAIP 2019: Liver Cancer Segmentation Challenge

Authors

Yoo Jung Kim, Hyungjoon Jang, Kyoungbun Lee, Seongkeun Park, Sung-Gyu Min, Choyeon Hong, Jeong Hwan Park, Kanggeun Lee, Jisoo Kim, Wonjae Hong, Hyun Jung, Yanling Liu, Haran Rajkumar, Mahendra Khened, Ganapathy Krishnamurthi, Sen Yang, Xiyue Wang, Chang Hee Han, Jin Tae Kwak, Jianqiang Ma, Zhe Tang, Bahram Marami, Jack Zeineh, Zixu Zhao, Pheng-Ann Heng, Rudiger Schmitz, Frederic Madesta, Thomas Rosch, Rene Werner, Jie Tian, Élodie Puybareau, Matteo Bovio, Xiufeng Zhang, Yifeng Zhu, Se Young Chun, Won-Ki Jeong, Peom Park, Jinwook Choi

Journal

Medical Image Analysis

Type

article

Date

2020-11-10

Abstract

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.

Bibtex (lrde.bib)

@Article{	  kim.20.media,
  title		= {{PAIP} 2019: {L}iver Cancer Segmentation Challenge},
  journal	= {Medical Image Analysis},
  volume	= {67},
  pages		= {101854},
  year		= {2021},
  month		= jan,
  issn		= {1361-8415},
  doi		= {10.1016/j.media.2020.101854},
  author	= {Yoo Jung Kim and Hyungjoon Jang and Kyoungbun Lee and
		  Seongkeun Park and Sung-Gyu Min and Choyeon Hong and Jeong
		  Hwan Park and Kanggeun Lee and Jisoo Kim and Wonjae Hong
		  and Hyun Jung and Yanling Liu and Haran Rajkumar and
		  Mahendra Khened and Ganapathy Krishnamurthi and Sen Yang
		  and Xiyue Wang and Chang Hee Han and Jin Tae Kwak and
		  Jianqiang Ma and Zhe Tang and Bahram Marami and Jack Zeineh
		  and Zixu Zhao and Pheng-Ann Heng and Rudiger Schmitz and
		  Frederic Madesta and Thomas Rosch and Rene Werner and Jie
		  Tian and \'Elodie Puybareau and Matteo Bovio and Xiufeng
		  Zhang and Yifeng Zhu and Se Young Chun and Won-Ki Jeong and
		  Peom Park and Jinwook Choi},
  keywords	= {Liver cancer, Tumor burden, Digital pathology, Challenge,
		  Segmentation},
  abstract	= {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.}
}