ArticleCancer imaging : the official publication of the International Cancer Imaging Society2015
Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review.Journal of digital imaging · 2018Pooled it
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- Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.AJNR. American journal of neuroradiology · 2022Review
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- Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging.Cancers · 2019Review
- Volumetric assessment of tumor size changes in pediatric low-grade gliomas: feasibility and comparison with linear measurements.Neuroradiology · 2018Article
- Deep Learning in Radiology: Does One Size Fit All?Journal of the American College of Radiology : JACR · 2018Article
- Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.Scientific data · 2017Article
- Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence.Journal of digital imaging · 2017Article
- Noninvasive amide proton transfer magnetic resonance imaging in evaluating the grading and cellularity of gliomas.Oncotarget · 2017Article
- Intra-rater variability in low-grade glioma segmentation.Journal of neuro-oncology · 2017Article
- Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning.Tomography (Ann Arbor, Mich.) · 2016Article
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8 authors.
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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 DISCUSSION: For 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.
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