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

Research output: Contribution to journalArticle

37 Scopus citations

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.

Original languageEnglish (US)
Article numbere0185844
JournalPLoS One
Volume12
Issue number10
DOIs
StatePublished - Oct 1 2017

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Fingerprint Dive into the research topics of 'A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery'. Together they form a unique fingerprint.

  • Cite this