Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications. (15th November 2016)

• Record Type: Journal Article
• Authors: Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun
• Is Part Of: Physics in medicine & biology. Volume 61:Number 24(2016:Dec.)
• Page Start: 8440
• Page End: 8461
• Publication Date: 2016-11-15
• Subjects: automatic segmentation -- brain metastases -- stereotactic radiosurgery -- contrast enhanced T1-weighted MRI; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/61/24/8440
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98 ± 0.01, an NMI of 0.97 ± 0.01, an SSIM of 0.999 ± 0.001, an HD of 2.2 ± 0.8 mm, an MSSD of 0.1 ± 0.1 mm, and an SDSSD of 0.3 ± 0.1 mm. The validation on the BRATS data resulted in a DC of 0.89 ± 0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86 ± 0.09, an NMI of 0.80 ± 0.11, an SSIM of 0.999 ± 0.001, an HD of 8.8 ± 12.6 mm, an MSSD of 1.5 ± 3.2 mm, and an SDSSD of 1.8 ± 3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.