CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Creators: name: Dorent, Reuben
name: Kujawa, Aaron
name: Ivory, Marina
name: Bakas, Spyridon
name: Rieke, Nicola
name: Joutard, Samuel
name: Glocker, Ben
name: Cardoso, Jorge
name: Modat, Marc
name: Batmanghelich, Kayhan
name: Belkov, Arseniy
name: Calisto, Maria Baldeon
name: Choi, Jae Won
name: Dawant, Benoit M.
name: Dong, Hexin
name: Escalera, Sergio
name: Fan, Yubo
name: Hansen, Lasse
name: Heinrich, Mattias P.
name: Joshi, Smriti
name: Kashtanova, Victoriya
name: Kim, Hyeon Gyu
name: Kondo, Satoshi
name: Kruse, Christian N.
name: Lai-Yuen, Susana K.
name: Li, Hao
name: Liu, Han
name: Ly, Buntheng
name: Oguz, Ipek
name: Shin, Hyungseob
name: Shirokikh, Boris
name: Su, Zixian
name: Wang, Guotai
name: Wu, Jianghao
name: Xu, Yanwu
name: Yao, Kai
name: Zhang, Li
name: Ourselin, Sebastien
name: Shapey, Jonathan
name: Vercauteren, Tom
Publisher: name: arXiv
Year: 2022
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice - VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice - VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.
description: In Medical Image Analysis