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Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardized MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms, especially when post-operative images are included. In this work, we show for the first time that automatic segmentation of VS on routine MRI datasets is also possible with high accuracy. We acquired and publicly release a curated multi-center routine clinical (MC-RC) dataset of 160 patients with a single sporadic VS. For each patient up to three longitudinal MRI exams with contrast-enhanced T1-weighted (ceT1w) (n = 124) and T2-weighted (T2w) (n = 363) images were included and the VS manually annotated. Segmentations were produced and verified in an iterative process: (1) initial segmentations by a specialized company; (2) review by one of three trained radiologists; and (3) validation by an expert team. Inter- and intra-observer reliability experiments were performed on a subset of the dataset. A state-of-the-art deep learning framework was used to train segmentation models for VS. Model performance was evaluated on a MC-RC hold-out testing set, another public VS datasets, and a partially public dataset. The generalizability and robustness of the VS deep learning segmentation models increased significantly when trained on the MC-RC dataset. Dice similarity coefficients (DSC) achieved by our model are comparable to those achieved by trained radiologists in the inter-observer experiment. On the MC-RC testing set, median DSCs were 86.2(9.5) for ceT1w, 89.4(7.0) for T2w, and 86.4(8.6) for combined ceT1w+T2w input images. On another public dataset acquired for Gamma Knife stereotactic radiosurgery our model achieved median DSCs of 95.3(2.9), 92.8(3.8), and 95.5(3.3), respectively. In contrast, models trained on the Gamma Knife dataset did not generalize well as illustrated by significant underperformance on theMC-RC routineMRI dataset, highlighting the importance of data variability in the development of robust VS segmentation models. The MC-RC dataset and all trained deep learning models were made available online. [ABSTRACT FROM AUTHOR]

Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models. Measurement(s) Vestibular Schwannoma Technology Type(s) Magnetic Resonance Imaging • image segmentation Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16528977 [ABSTRACT FROM AUTHOR]

Introduction: Fully automated artificial intelligence (AI) frameworks have recently reached outstanding performance for automatic segmentation of vestibular schwannomas (VS) from MRI. However, they often generalize poorly when the training data is acquired with scanners or imaging sequences that are different from those of the testing data. This problem strongly reduces the applicability of AI frameworks in real world radiosurgery settings. To increase the robustness of AI tools, various technical approaches have been proposed. However, these techniques have been validated either on private datasets or on small publicly available datasets. To tackle these limitations, the authors organized the CrossModality Domain Adaptation (crossMoDA) challenge in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). Methods: The challenge’s goal was to segment two key brain structures involved in the follow-up and treatment planning of VS: the tumor and the cochlea. While contrast-enhanced T1 (ceT1) scans are commonly used for VS segmentation, recent work has demonstrated that high-resolution T2 (hrT2) imaging could be a reliable, safer, and lower-cost alternative to ceT1. For these reasons, the authors proposed an unsupervised cross-modality challenge to benchmark the generalization capability of techniques developed on images acquired with one sequence (ceT1) and tested on images acquired with another one (hrT2). Specifically, participants had access to a training set of unpaired annotated ceT1 (N=105) and non-annotated hrT2 (N=105). The automated segmentation tools developed by the challenge participants were then tested on a private hrT2 evaluation set (N=137). Images were collected on consecutive patients with a single sporadic VS treated with Gamma Knife stereotactic radiosurgery. Results: A total of 341 teams registered for the challenge, allowing them to download the data. 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 countries submitted their algorithm for the evaluation phase. The level of performance reached by the topperforming teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to a state-of-the-art model developed using hrT2 scans and their corresponding annotations (median Dice score - VS: 92.5%; Cochleas: 87.7%). Conclusions: The authors organized the first international benchmark assessing the robustness of AI frameworks for stereotactic surgery planning of VS. The excellent results obtained by the top-performing teams suggest that AI tools can be robust to different imaging sequences. The next challenge edition will assess the robustness of AI tools for Koos grade classification. [ABSTRACT FROM AUTHOR]

Introduction: In an earlier study by King’s College London (KCL), a framework for the automatic segmentation of vestibular schwannomas (VS) from MRI scans was proposed. They demonstrated that artificial intelligence is capable of annotating and calculating VS tumor volumes, which can benefit tumor progression monitoring. In this study, their algorithm is validated using data from another institution. The obtained results are subsequently compared to a multi-institutional model, in which the data from both centers is combined. Methods: In addition to the original KCL dataset of 375 contrast-enhanced T1-weighted (ceT1) MRI scans, a total of 1,115 ceT1 scans of individual VS patients from the ETZ hospital in Tilburg, the Netherlands, were used. All tumors were manually annotated by a neurosurgeon at time of treatment. Employing the original 2.5D convolutional neural network, two different models were subsequently trained. First, one model is trained solely on 176 scans from the KCL dataset, using an additional 20 and 46 scans for hyper-parameter tuning and internal validation, respectively. This single-institution model is externally validated on the entire ETZ dataset. The second model employed 242 scans from the KCL dataset and all of the ETZ data from before 2015 (733 scans), with the remainder of the ETZ data equally distributed between a tuning and validation set. The remaining 133 scans from the KCL dataset were also used for validation. Results: The single-institutional model achieved internal and external validation mean Dice scores of 92.0±5.1% and 64.5±32.4%, respectively. The external validation set included 175 scans where the model failed to recognize any VS, resulting in a Dice score of zero for these scans, which skews the results. The second model yielded validation mean dice scores of 92.5±5.1% for the ETZ scans and 89.1±9.6% for the KCL scans. During validation, most of the low-scoring segmentations are of tumors that are either very small or cystic. Conclusions: The significant difference in performance between internal and external validation of the first model, thereby surmising a poor generalization, can be explained by variations in spatial resolution and image acquisition parameters between the two datasets. The second model shows performances approaching inter-observer variability of human annotators (cf. 93.8±3.1%). The results show that creating a robust and well-generalizing model from single-institution data is challenging. However, the multi-institutional results empower the earlier demonstrated capabilities of the framework for the automatic segmentation of VS. [ABSTRACT FROM AUTHOR]

• An efficient approach for learning joint tissue and brain lesion segmentation. • A mathematically grounded training strategy for transferring information across tasks with missing modalities. • The integration of various domain adaptation techniques into the framework to deal with domain-shifted datasets. • An extensive evaluation on white matter lesions and gliomas. Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on task-specific imaging protocols including a task-specific set of imaging modalities. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from aggregated task-specific hetero-modal domain-shifted and partially-annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper bound of the risk to deal with heterogeneous imaging modalities across datasets. To deal with potential domain shift, we integrated and tested three conventional techniques based on data augmentation, adversarial learning and pseudo-healthy generation. For each individual task, our joint approach reaches comparable performance to task-specific and fully-supervised models. The proposed framework is assessed on two different types of brain lesions: White matter lesions and gliomas. In the latter case, lacking a joint ground-truth for quantitative assessment purposes, we propose and use a novel clinically-relevant qualitative assessment methodology. [ABSTRACT FROM AUTHOR]