Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. This study evaluated deep learning algorithms for semantic segmentation and quantification of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular hemorrhage (IVH) on noncontrast CT (NCCT) scans of patients with spontaneous ICH. Models were assessed on 1,732 annotated baseline NCCT scans obtained from the TICH-2 international multicenter trial (ISRCTN93732214), and different loss functions using three-dimensional nnU-Net were examined to address class imbalance (30% of participants with IVH in dataset).