Cross-Modality Domain Adaptation for Medical Image Segmentation
Creators: name: Dorent, Reuben
name: Kujawa, Aaron
name: Shapey, Jonathan
name: Joutard, Samuel
name: Cardoso, Jorge
name: Modat, Marc
name: Rieke, Nicola
name: Glocker, Ben
name: Bakas, Spyridon
name: Vercauteren, Tom
Publisher: name: Zenodo
Year: 2021
Subjects: Domain Adaptation
Segmentation
Cross-Modality

description: Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets [4,5] or on small publicly available datasets [6,7,8,9]. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduces the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the tumour and the cochlea. Specifically, the segmentation of the tumour and the surrounding organs at risk, such as the cochlea, is required for radiosurgery, a common VS treatment. Moreover, tumour volume measurement has also been shown to be the most accurate measurements for the evaluation of VS growth [3]. While contrast-enhanced T1 (ceT1) Magnetic Resonance Imaging (MRI) scans are commonly used for VS segmentation, recent work [1,2] has demonstrated that high-resolution T2 (hrT2) imaging could be a reliable, safer, and lower-cost alternative to ceT1. For these reasons,<br> we propose an unsupervised cross-modality challenge (from ceT1 to hrT2) that aims to automatically perform VS and cochlea segmentation on hrT2 scans. The training source and target sets are respectively unpaired annotated ceT1 and non-annotated hrT2 scans. This challenge will be the first medical segmentation benchmark of unsupervised DA techniques and promote the development of new unsupervised domain adaptation solutions for medical image segmentation. It will also contribute to the development of new algorithms for the follow-up and treatment planning of VS using hrT2 scans only. <strong>References</strong> [1] Shapey, J., et al: An artificial intelligence framework for automatic segmentation and volumetry of Vestibular Schwannomas from contrast-enhanced t1-weighted and high-resolution t2-weighted MRI. In: Journal of Neurosurgery JNS. (2019) [2] Wang, G., et al: Automatic segmentation of Vestibular Schwannoma from T2-weighted MRI by deep spatial attention with hardness-weighted loss. In: MICCAI 2019. (2019) [3] Van de Langenberg, R., et al: Follow-up assessment of vestibular schwannomas: volume quantification versus two-dimensional measurements. In: Neuroradiology 51, 517 (2009). [4] Kamnitsas, K., et al: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: IPMI (2017) [5] Yang, J., et al: Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation. In: MICCAI 2019. (2019) [6] Chen, C., el al: Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation. In: AAAI-19. (2019) [7] Dou, Q., et al: Pnp-adanet: Plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation. ArXiv. (2018) [8] Orbes-Arteaga, et al: Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. In: DART - Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. (2019) [9] Xue, Y., el al: Dual-task Self-supervision for Cross-Modality Domain Adaptation. In: MICCAI 2020. (2020) [10] Maier-Hein, L., Eisenmann, M., et al: “Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions”. ArXiv:1806.02051, (2018) [11] Milchenko, M., Marcus, D. Obscuring Surface Anatomy in Volumetric Imaging Data. In: Neuroinform 11, 65–75 (2013). [12] Bakas, S., et al: "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge". ArXiv:1811.02629 (2018).