ArticleBMC medical imaging2019
A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients.
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 7 papers.
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7 citing papers in PubMed, 21 citations in OpenAlex.
- Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.BMC medical imaging · 2021Article
- MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.BMC medical imaging · 2021Article
- Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint.Frontiers in neuroscience · 2021Article
- A Transfer Model Based on Supervised Multi-Layer Dictionary Learning for Brain Tumor MRI Image Recognition.Frontiers in neuroscience · 2021Article
- Deep semi-supervised learning for brain tumor classification.BMC medical imaging · 2020Article
- Hybrid computed tomography and magnetic resonance imaging 3D printed models for neurosurgery planning.Annals of translational medicine · 2019Review
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
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7 authors at 3 institutions in 1 country.
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Abstract
backgroundAccurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency.
methodsFour MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice's similarity coefficient (DSC) and Sensitivity and Specificity.
resultsExperimental study with the five patients' data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets.
conclusionsEfficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy.
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