Abstract
BackgroundSegmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images.MethodsFirst, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts’ segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations.Results and discussionFor 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts’ manual segmentation results.ConclusionsWe present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.
Highlights
Magnetic resonance (MR) imaging is a non-invasive medical imaging technique that provides excellent soft tissue contrast and has become the standard imaging technique for brain tumor diagnosis [1]
Pre-operative imaging for low-grade gliomas (LGGs) is less consistent and all imaging types are not always available; we have found that only pre- or post-contrast T1-weighted (T1W) and T2weighted (T2W) MR images are consistently available for pre-operative LGGs
We present a combination of classification and region based methods using atlas prior information for segmentation of pre-operative LGGs from T2W alone or T2W plus post-contrast T1W MR images
Summary
Magnetic resonance (MR) imaging is a non-invasive medical imaging technique that provides excellent soft tissue contrast and has become the standard imaging technique for brain tumor diagnosis [1]. Kaus et al [15] presented a template-driven classification technique that involves iterative statistical segmentation of LGGs based on T1W MR image intensity values This method requires manual selection of four points for each tissue type for initialization of the algorithm. Segmentation of LGGs is challenging because they rarely enhance after gadolinium administration Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. They have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images
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