@inproceedings{abdad3c6f43544b1bfd32018ec2664cc,
title = "Whole brain segmentation and labeling from CT using synthetic MR images",
abstract = "To achieve whole-brain segmentation—i.e., classifying tissues within and immediately around the brain as gray matter (GM), white matter (WM), and cerebrospinal fluid—magnetic resonance (MR) imaging is nearly always used. However, there are many clinical scenarios where computed tomography (CT) is the only modality that is acquired and yet whole brain segmentation (and labeling) is desired. This is a very challenging task, primarily because CT has poor soft tissue contrast; very few segmentation methods have been reported to date and there are no reports on automatic labeling. This paper presents a whole brain segmentation and labeling method for non-contrast CT images that first uses a fully convolutional network (FCN) to synthesize an MR image from a CT image and then uses the synthetic MR image in a standard pipeline for whole brain segmentation and labeling. The FCN was trained on image patches derived from ten co-registered MR and CT images and the segmentation and labeling method was tested on sixteen CT scans in which co-registered MR images are available for performance evaluation. Results show excellent MR image synthesis from CT images and improved soft tissue segmentation and labeling over a multi-atlas segmentation approach.",
keywords = "CNN, CT, Deep learning, FCN U-net, MR, Segmentation, Synthesis",
author = "Can Zhao and Aaron Carass and Junghoon Lee and Yufan He and Prince, {Jerry Ladd}",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-67389-9_34",
language = "English (US)",
isbn = "9783319673882",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "291--298",
booktitle = "Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",
note = "8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 10-09-2017 Through 10-09-2017",
}