@inproceedings{04f7c399c80644479c85845afc02de48,
title = "Shortcomings of ventricle segmentation using deep convolutional networks",
abstract = "Normal Pressure Hydrocephalus (NPH) is a brain disorder that can present with ventriculomegaly and dementia-like symptoms, which often can be reversed through surgery. Having accurate segmentation of the ventricular system into its sub-compartments from magnetic resonance images (MRI) would be beneficial to better characterize the condition of NPH patients. Previous segmentation algorithms need long processing time and often fail to accurately segment severely enlarged ventricles in NPH patients. Recently, deep convolutional neural network (CNN) methods have been reported to have fast and accurate performance on medical image segmentation tasks. In this paper, we present a 3D U-net CNN-based network to segment the ventricular system in MRI. We trained three networks on different data sets and compared their performances. The networks trained on healthy controls (HC) failed in patients with NPH pathology, even in patients with normal appearing ventricles. The network trained on images from HC and NPH patients provided superior performance against state-of-the-art methods when evaluated on images from both data sets.",
keywords = "CNN, Hydrocephalus, MRI, Segmentation",
author = "Muhan Shao and Shuo Han and Aaron Carass and Xiang Li and Blitz, {Ari M.} and Prince, {Jerry L.} and Ellingsen, {Lotta M.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 1st International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, 1st International Workshop on Deep Learning Fails, DLF 2018, and 1st International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",
year = "2018",
doi = "10.1007/978-3-030-02628-8_9",
language = "English (US)",
isbn = "9783030026271",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "79--86",
editor = "Zeike Taylor and Mauricio Reyes and Cardoso, {M. Jorge} and Silva, {Carlos A.} and Danail Stoyanov and Lena Maier-Hein and Sergio Pereira and Kia, {Seyed Mostafa} and Ipek Oguz and Bennett Landman and Anne Martel and Edouard Duchesnay and Tommy Lofstedt and Marquand, {Andre F.} and Raphael Meier",
booktitle = "Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings",
}