CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

Autor: Kavur AE; Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Turkey., Gezer NS; Department of Radiology, Faculty Of Medicine, Dokuz Eylul University, Izmir, Turkey., Barış M; Department of Radiology, Faculty Of Medicine, Dokuz Eylul University, Izmir, Turkey., Aslan S; Ca' Foscari University of Venice, ECLT and DAIS, Venice, Italy; Ege University, International Computer Institute, Izmir, Turkey., Conze PH; IMT Atlantique, LaTIM UMR 1101, Brest, France., Groza V; Median Technologies, Valbonne, France., Pham DD; Intelligent Systems, Faculty of Engineering, University of Duisburg-Essen, Germany., Chatterjee S; Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany; Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany., Ernst P; Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany., Özkan S; Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey., Baydar B; Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey., Lachinov D; Department of Ophthalmology and Optometry, Medical Uni. of Vienna, Austria., Han S; Johns Hopkins University, Baltimore, USA., Pauli J; Intelligent Systems, Faculty of Engineering, University of Duisburg-Essen, Germany., Isensee F; Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany., Perkonigg M; CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna, Austria., Sathish R; Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, India., Rajan R; School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India., Sheet D; Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, India., Dovletov G; Intelligent Systems, Faculty of Engineering, University of Duisburg-Essen, Germany., Speck O; Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany., Nürnberger A; Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany., Maier-Hein KH; Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany., Bozdağı Akar G; Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey., Ünal G; Faculty of Computer and Informatics Engineering, İstanbul Technical University, İstanbul, Turkey., Dicle O; Department of Radiology, Faculty Of Medicine, Dokuz Eylul University, Izmir, Turkey., Selver MA; Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey. Electronic address: alper.selver@deu.edu.tr.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2021 Apr; Vol. 69, pp. 101950. Date of Electronic Publication: 2020 Dec 25.
DOI: 10.1016/j.media.2020.101950
Abstrakt: Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance are hard to interpret. This makes comparative analysis a necessary tool towards interpretable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal semantic segmentation tasks has been rarely discussed. In order to expand the knowledge on these topics, the CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Abdominal organ segmentation from routine acquisitions plays an important role in several clinical applications, such as pre-surgical planning or morphological and volumetric follow-ups for various diseases. These applications require a certain level of performance on a diverse set of metrics such as maximum symmetric surface distance (MSSD) to determine surgical error-margin or overlap errors for tracking size and shape differences. Previous abdomen related challenges are mainly focused on tumor/lesion detection and/or classification with a single modality. Conversely, CHAOS provides both abdominal CT and MR data from healthy subjects for single and multiple abdominal organ segmentation. Five different but complementary tasks were designed to analyze the capabilities of participating approaches from multiple perspectives. The results were investigated thoroughly, compared with manual annotations and interactive methods. The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0.98 ± 0.00 / 0.95 ± 0.01), but the best MSSD performance remains limited (21.89 ± 13.94 / 20.85 ± 10.63 mm). The performances of participating models decrease dramatically for cross-modality tasks both for the liver (DICE: 0.88 ± 0.15 MSSD: 36.33 ± 21.97 mm). Despite contrary examples on different applications, multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones (performance drop around 5%). Nevertheless, some of the successful models show better performance with their multi-organ versions. We conclude that the exploration of those pros and cons in both single vs multi-organ and cross-modality segmentations is poised to have an impact on further research for developing effective algorithms that would support real-world clinical applications. Finally, having more than 1500 participants and receiving more than 550 submissions, another important contribution of this study is the analysis on shortcomings of challenge organizations such as the effects of multiple submissions and peeking phenomenon.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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