Abstract
Brain magnetic resonance image segmentation is one of the most important tasks in medical image analysis and has considerable importance to the effective use of medical imagery in clinical and surgical setting. In particular, the tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain measurement and disease diagnosis. A variety of studies have shown that the learning-based techniques are efficient and effective in brain tissue segmentation. However, the learning-based segmentation methods depend largely on the availability of good training labels. The commonly used 3T magnetic resonance (MR) images have insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF, therefore not able to provide good training labels for learning-based methods. The advances in ultra-high field 7T imaging make it possible to acquire images with an increasingly high level of quality. In this study, we propose an algorithm based on random forest for segmenting 3T MR images by introducing the segmentation information from their corresponding 7T MR images (through semi-automatic labeling). Furthermore, our algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers to improve the tissue segmentation. Experimental results on 10 subjects with both 3T and 7T MR images in a leave-one-out validation, show that the proposed algorithm performs much better than the state-of-the-art segmentation methods.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings |
Editors | Li Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang |
Publisher | Springer Verlag |
Pages | 213-220 |
Number of pages | 8 |
ISBN (Print) | 9783319471563 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 17 → 2016 Oct 17 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10019 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 |
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Country/Territory | Greece |
City | Athens |
Period | 16/10/17 → 16/10/17 |
Bibliographical note
Funding Information:This work was supported by the China Scholarship Council (No. 201506840071) and the Research Fund for the Doctoral Program of Higher Education of China (RFDP) (No. 20133219110029).
Publisher Copyright:
© Springer International Publishing AG 2016.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science