Deep Learning-based Multiclass Segmentation in Aneurysmal Subarachnoid Hemorrhage

Julia Kiewitz, Orhun Utku Aydin, Adam Hilbert, Marie Gultom, Anouar Nouri, Ahmed A Khalil, Peter Vajkoczy, Satoru Tanioka, Fujimaro Ishida, Nora F. Dengler, Dietmar Frey
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Abstract

Introduction Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with a significant variability in patients’ outcomes. Radiographic scores used to assess the extent of SAH or other potentially outcome-relevant pathologies are limited by interrater variability and do not utilize all available information from the imaging. Image segmentation plays an important role in extracting relevant information from images by enabling precise identification and delineation of objects or regions of interest. Thus, segmentation offers the potential for automatization of score assessments and downstream outcome prediction using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aSAH outcome prediction.
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基于深度学习的动脉瘤性蛛网膜下腔出血多类分割技术
导言 动脉瘤性蛛网膜下腔出血(aSAH)是一种危及生命的疾病,患者的预后差异很大。用于评估 SAH 或其他可能与预后相关的病变程度的影像评分受限于评定者之间的差异,而且无法利用影像中的所有可用信息。图像分割通过精确识别和划分感兴趣的对象或区域,在从图像中提取相关信息方面发挥着重要作用。因此,图像分割可利用精确的容积信息实现评分评估和下游结果预测的自动化。我们的研究旨在开发一种深度学习模型,该模型可对与急性脑梗死结果预测相关的结构和病理进行自动多类分割。
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