Title |
A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery
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Published in |
PLOS ONE, October 2017
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DOI | 10.1371/journal.pone.0185844 |
Pubmed ID | |
Authors |
Yan Liu, Strahinja Stojadinovic, Brian Hrycushko, Zabi Wardak, Steven Lau, Weiguo Lu, Yulong Yan, Steve B. Jiang, Xin Zhen, Robert Timmerman, Lucien Nedzi, Xuejun Gu |
Abstract |
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. |
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Unknown | 1 | 100% |
Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 174 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 26 | 15% |
Researcher | 21 | 12% |
Student > Master | 20 | 11% |
Student > Bachelor | 13 | 7% |
Professor > Associate Professor | 9 | 5% |
Other | 24 | 14% |
Unknown | 61 | 35% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 26 | 15% |
Computer Science | 23 | 13% |
Engineering | 15 | 9% |
Physics and Astronomy | 7 | 4% |
Neuroscience | 6 | 3% |
Other | 19 | 11% |
Unknown | 78 | 45% |