ArticleBMC medical imaging2019
Automatic glioma segmentation based on adaptive superpixel.
Article in BMC medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 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
- Exploring the Impact of 3D Fast Spin Echo and Inversion Recovery Gradient Echo Sequences Magnetic Resonance Imaging Acquisition on Automated Brain Tumor Segmentation.Mayo Clinic proceedings. Digital health · 2024Article
- Green synthesis of silver and iron oxide nanoparticles mediated photothermal effects on Blastocystis hominis.Lasers in medical science · 2024Article
- A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.Medical physics · 2023Article
- Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue.Biomedical optics express · 2023Article
- Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review.Cancers · 2022Review
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm.Tomography (Ann Arbor, Mich.) · 2022Article
- Review
- Optimization of psoriasis assessment system based on patch images.Scientific reports · 2021Article
- Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.BMC medical imaging · 2021Article
- Deep semi-supervised learning for brain tumor classification.BMC medical imaging · 2020Article
- Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.Neuroradiology · 2020Review
- Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe automatic glioma segmentation is of great significance for clinical practice. This study aims to propose an automatic method based on superpixel for glioma segmentation from the T2 weighted Magnetic Resonance Imaging.
methodsThe proposed method mainly includes three steps. First, we propose an adaptive superpixel generation algorithm based on simple linear iterative clustering version with 0 parameter (ASLIC0). This algorithm can acquire a superpixel image with fewer superpixels and better fit the boundary of region of interest (ROI) by automatically selecting the optimal number of superpixels. Second, we compose a training set by calculating the statistical, texture, curvature and fractal features for each superpixel. Third, Support Vector Machine (SVM) is used to train classification model based on the features of the second step.
resultsThe experimental results on Multimodal Brain Tumor Image Segmentation Benchmark 2017 (BraTS2017) show that the proposed method has good segmentation performance. The average Dice, Hausdorff distance, sensitivity, and specificity for the segmented tumor against the ground truth are 0.8492, 3.4697 pixels, 81.47, and 99.64%, respectively. The proposed method shows good stability on high- and low-grade glioma samples. Comparative experimental results show that the proposed method has superior performance.
conclusionsThis provides a close match to expert delineation across all grades of glioma, leading to a fast and reproducible method of glioma segmentation.
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