ArticleFrontiers in neuroscience2013
Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors.
Article in Frontiers in neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury.NeuroImage. Clinical · 2024Review
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Accuracy and practical aspects of semi- and fully automatic segmentation methods for resected brain areas.Neuroradiology · 2020Article
- Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.International journal of computer assisted radiology and surgery · 2018Article
- Linking left hemispheric tissue preservation to fMRI language task activation in chronic stroke patients.Cortex; a journal devoted to the study of the nervous system and behavior · 2017Article
- Automated lesion detection on MRI scans using combined unsupervised and supervised methods.BMC medical imaging · 2015Article
- Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.Neuro-oncology advancesArticle
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Authors and funding
10 authors.
Funding
Abstract
Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit "extra prior" for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.
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