ArticleJournal of healthcare engineering2017
Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.European radiology · 2021Pooled it
- Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.International journal of computer assisted radiology and surgery · 2021Pooled it
- Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy.Scientific reports · 2025Article
- Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review.Journal of Korean Neurosurgical Society · 2023Article
- A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.Medical physics · 2023Article
- A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges.Current neuropharmacology · 2022Review
- Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?Medical oncology (Northwood, London, England) · 2021Review
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts.Journal of digital imaging · 2019Article
- Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.European radiology · 2019Article
- Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine.Frontiers in oncology · 2019Review
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7 authors.
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Abstract
This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.
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