arXiv subjects

Tobias Boeckh-Behrens

Publications and source records attributed to Tobias Boeckh-Behrens.

3 recordsLinked to original sources

arXiv2025

ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset

Stroke remains a leading cause of global morbidity and mortality, imposing a heavy socioeconomic burden. Advances in endovascular reperfusion therapy and CT and MR imaging for treatment guidance have significantly improved patient outcomes. Developing machine learning algorithms that can create accurate models of brain function from stroke images for tasks like lesion identification and tissue survival prediction requires large, diverse, and well annotated public datasets. While several high-quality image datasets in stroke exist, they include only single time point data. Data over different time points are essential to accurately identify lesions and predict prognosis. Here, we provide comprehensive longitudinal stroke data, including (sub-)acute CT imaging with angiography and perfusion, follow-up MRI after 2-9 days, and acute and longitudinal clinical data up to a three-month outcome. The dataset also includes vessel occlusion masks from acute CT angiography and delineated infarction masks in follow-up MRI. This multicenter dataset consists of 245 cases and is a solid basis for developing powerful machine-learning algorithms to facilitate clinical decision-making.

cs.CV↗

arXiv2024

Numerical simulation of individual coil placement -- A proof-of-concept study for the prediction of recurrence after aneurysm coiling

Rupture of intracranial aneurysms results in severe subarachnoidal hemorrhage, which is associated with high morbidity and mortality. Neurointerventional occlusion of the aneurysm through coiling has evolved to a therapeutical standard. The choice of the specific coil has an important influence on secondary regrowth requiring retreatment. Aneurysm occlusion was simulated either through virtual implantation of a preshaped 3D coil or with a porous media approach. In this study, we used a recently developed numerical approach to simulate aneurysm shapes in specific challenging aneurysm anatomies and correlated these with aneurysm recurrence 6 months after treatment. The simulation showed a great variety of coil shapes depending on the variability in possible microcatheter positions. Aneurysms with a later recurrence showed a tendency for more successful coiling attempts. Results revealed further trends suggesting lower simulated packing densities in aneurysms with reoccurrence. Simulated packing densities did not correlate with those calculated by conventional software, indicating the potential for our approach to offer additional predictive value. Our study, therefore, pioneers a comprehensive numerical model for simulating aneurysm coiling, providing insights into individualized treatment strategies and outcome prediction. Future directions involve expanding the model's capabilities to simulate intraprocedural outcomes and long-term predictions, aiming to refine occlusion quality criteria and validate prediction parameters in larger patient cohorts. This simulation framework holds promise for enhancing clinical decision-making and optimizing patient outcomes in endovascular aneurysm treatment.

cs.CE↗

arXiv2022

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duration of hospital stay to predict outcome by visualizing infarct core size and location. Furthermore, it may be used to characterize stroke etiology, e.g. differentiation between (cardio)-embolic and non-embolic stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. Previous iterations of the Ischemic Stroke Lesion Segmentation (ISLES) challenge have aided in the generation of identifying benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions. This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n=250 and a test dataset of n=150. All training data will be made publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge with the goal of finding algorithmic methods to enable the development and benchmarking of robust and accurate segmentation algorithms for ischemic stroke.

cs.CV↗