PAIP 2019: Liver cancer segmentation challenge

Kim, Yoo Jung; Jang, Hyungjoon; Lee, Kyuho; Park, Seongkeun; Min, Sung Gyu; Hong, Choyeon; Park, Jeong Hwan; Lee, Kanggeun; Kim, Ji-Soo; 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, René; Tian, Jie; Puybareau, Élodie; Bovio, Matteo; Zhang, Xiufeng; Zhu, Yifeng; Chun, Se Young; Jeong, Won Ki; Park, Peom; Choi, Jinwook

doi:10.1016/j.media.2020.101854

Cited by 70 publications

(27 citation statements)

References 12 publications

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“…Only ten WSIs were annotated on pixel-level whereas all remaining images of the dataset were labeled on slide level. In 2019, the Pathology Artificial Intelligence Platform (PAIP) 16 dataset was published, containing 100 liver WSIs with annotations of the overall tumor area and the viable tumor area within.…”

Section: Background and Summarymentioning

confidence: 99%

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Wilm¹,

Fragoso²,

Marzahl³

et al. 2022

Preprint

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Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset consisting of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes including seven cutaneous tumor subtypes. Regarding sample size and annotation extent, this exceeds most publicly available datasets which are oftentimes limited to the tumor area or merely provide patch-level annotations. We validated our model for tissue segmentation, achieving a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For tumor subtype classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors, we believe that the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.

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Section: Background and Summarymentioning

confidence: 99%

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Wilm¹,

Fragoso²,

Marzahl³

et al. 2022

Preprint

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“…The statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality. Furthermore, [ 53 , 54 ] also using the optimization algorithms for medical image segmentation. The researchers in [ 55 ] proposed a multilevel thresholding method for medical image segmentation based on a partitioned and cooperative quantum-behaved PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Houssein

Emam

Ali

2021

Neural Comput & Applic

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Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.

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“…Finally, out-of-domain performance was assessed on Formalin-fixed paraffinembedded (FFPE) slides from 2 open-source datasets with public pixelwise ground-truth: DigestPath for colon adenocarcinoma [16], and PAIP2019 for liver hepatocellular carcinoma [13]. FFPE tissue displays dissimilarity with frozen tissue, therefore providing insight about out-of-domain generalization.…”

Section: Datasetsmentioning

confidence: 99%

Weakly supervised pan-cancer segmentation tool

Lerousseau¹,

Classe²,

Battistella³

et al. 2021

Preprint

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The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expert variability. To address these issues, recent approaches have leveraged categorical annotations at the slide-level, that in general suffer from robustness and generalization. In this paper, we propose a novel weakly supervised multi-instance learning approach that deciphers quantitative slide-level annotations which are fast to obtain and regularly present in clinical routine. The extreme potentials of the proposed approach are demonstrated for tumor segmentation of solid cancer subtypes. The proposed approach achieves superior performance in out-of-distribution, out-of-location, and out-of-domain testing sets.

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PAIP 2019: Liver cancer segmentation challenge

Cited by 70 publications

References 12 publications

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Weakly supervised pan-cancer segmentation tool

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