Abstract
In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.
Original language | English (US) |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings |
Editors | Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 730-739 |
Number of pages | 10 |
ISBN (Print) | 9783030597276 |
DOIs | |
State | Published - 2020 |
Event | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: Oct 4 2020 → Oct 8 2020 |
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 | 12267 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 |
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Country/Territory | Peru |
City | Lima |
Period | 10/4/20 → 10/8/20 |
Bibliographical note
Funding Information:Acknowledgements. This work was supported by NIH grants P41 EB027061, P41 EB015894, and P30 NS076408; Investissements d’avenir ANR-10-IAIHU-06 and ANR-11-INBS-0006; INCa-DGOS-Inserm_12560 (SiRIC CURAMUS)
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Keywords
- Brain cancer
- Image guided intervention
- Medical image segmentation
Center for Magnetic Resonance Research (CMRR) tags
- SMCT
- CTR