A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

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 (+1 others)
2017 PLoS ONE  
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
more » ... ntation 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. OPEN ACCESS Citation: Liu Y, Stojadinovic S, Hrycushko B, Wardak Z, Lau S, Lu W, et al. (2017) A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS ONE 12(10): e0185844. https://doi.org/10. Fig 12. ROC curve of the En-DeepMedic with different local patch size. https://doi.org/10.1371/journal.pone.0185844.g012 A convolutional neural network-based automatic delineation strategy for brain metastases PLOS ONE | https://doi.
doi:10.1371/journal.pone.0185844 pmid:28985229 pmcid:PMC5630188 fatcat:64c577egijaohhndrd3kcjawfy