Date

2021-04-01

Author

Kavur, A. Emre
Gezer, N. Sinem
Baris, Mustafa
ASLAN, SİNEM
Conze, Pierre-Henri
Groza, Vladimir
Duc Duy Pham, Duc Duy Pham
Chatterjee, Soumick
Ernst, Philipp
Ozkan, Savas
Baydar, Bora
Lachinov, Dmitry
Han, Shuo
Pauli, Josef
Isensee, Fabian
Perkonigg, Matthias
Sathish, Rachana
Rajan, Ronnie
Sheet, Debdoot
Dovletov, Gurbandurdy
Speck, Oliver
Nurnberger, Andreas
Maier-Hein, Klaus H.
Akar, Gözde
Ünal, Gözde
DİCLE, OĞUZ
SELVER, MUSTAFA ALPER

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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 anal-ysis 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 & ndash; Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge was organized in conjunction with the IEEE Interna-tional 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. Pre-vious 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 de-signed 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. (c) 2020 Elsevier B.V. All rights reserved.

Subject Keywords

Segmentation, Challenge, Abdomen, Cross-modality

URI

https://hdl.handle.net/11511/90360

Collections

Department of Electrical and Electronics Engineering, Article