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, 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 introduced the first large and multi-class dataset for unsupervised crossmodality Domain Adaptation. Compared to the previous crossMoDA instance, which made use of singleinstitution data and featured a single segmentation task, the 2022 edition extends the segmentation task by including multi-institutional data and introduces a new classification task. The goal of the segmentation task (Task 1) is to segment two key brain structures (tumour and cochlea) involved in the follow-up and treatment planning of vestibular schwannoma (VS). The segmentation of these two structures is required for radiosurgery, a common VS treatment. Moreover, tumour volume measurement has also been shown to be the most accurate measurement for the evaluation of VS growth. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging, as it mitigates the risks associated with gadolinium-containing contrast agents [1,2]. In addition to improving patient safety, hrT2 imaging is 10 times more cost-efficient than ceT1 imaging [17]. For this reason, we proposed an unsupervised cross-modality segmentation benchmark (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 from both pre-operative and post-operative time points. To validate the robustness of the proposed approaches on different hrT2 settings, multi-institutional scans from centres in London, UK and Tilburg, NL are used in this task. The goal of the classification task (Task 2) is to automatically classify hrT2 images with VS according to the Koos grade [14]. The Koos grading scale is a classification system for VS that characterises the tumour and its impact on adjacent brain structures (e.g., brain stem, cerebellum). The Koos classification is commonly determined to decide on the treatment plan (surveillance, radiosurgery, open surgery). Similarly to the VS segmentation, Koos grading is currently performed on ceT1 scans, but hrT2 could be used. For this reason, we propose an unsupervised crossmodality classification benchmark (from ceT1 to hrT2) that aims to automatically determine the Koos grade on hrT2 scans. Only pre-operative data is used for this task. Again, multi-institutional scans from centres in London, UK and Tilburg, NL are used in this task. Participants are free to choose whether they want to focus only on one or both tasks and use the data from one task for the other task. References [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 crossmodality 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). [13] Maier, O., et al: "ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI". Medical Image Analysis (2017). 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