@inproceedings{24146b8673a64189a6449b9d4f25ba86,
title = "Investigation of probability maps in deep-learning-based brain ventricle parcellation",
abstract = "Normal Pressure Hydrocephalus (NPH) is a brain disorder associated with ventriculomegaly. Accurate segmentation of the ventricle system into its sub-compartments from magnetic resonance images (MRIs) could help evaluate NPH patients for surgical intervention. In this paper, we modify a 3D U-net utilizing probability maps to perform accurate ventricle parcellation, even with grossly enlarged ventricles and post-surgery shunt artifacts, from MRIs. Our method achieves a mean dice similarity coefficient (DSC) on whole ventricles for healthy controls of 0.864 ± 0.047 and 0.961 ± 0.024 for NPH patients. Furthermore, with the benefit of probability maps, the proposed method provides superior performance on MRI with grossly enlarged ventricles (mean DSC value of 0.965 ± 0.027) or post-surgery shunt artifacts (mean DSC value of 0.964 ± 0.031). Results indicate that our method provides a high robust parcellation tool on the ventricular systems which is comparable to other state-of-the-art methods.",
keywords = "MRI, normal pressure hydrocephalus, probability map, ventricle parcellation",
author = "Yuli Wang and Anqi Feng and Yuan Xue and Muhan Shao and Blitz, {Ari M.} and Luciano, {Mark G.} and Aaron Carass and Prince, {Jerry L.}",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Medical Imaging 2023: Image Processing ; Conference date: 19-02-2023 Through 23-02-2023",
year = "2023",
doi = "10.1117/12.2653999",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Olivier Colliot and Ivana Isgum",
booktitle = "Medical Imaging 2023",
}