ArticleBiomedical signal processing and control2020
Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion.
Article in Biomedical signal processing and control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 67 citations in OpenAlex.
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.PloS one · 2026Article
- A Novel Fragmentation-based Approach for Accurate Segmentation of Small-Sized Brain Tumors in MRI Images.Current medical imaging · 2025Article
- Multifractal Analysis of Brain Tumor Interface in Glioblastoma.Advances in neurobiology · 2024Article
- Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.PeerJ. Computer science · 2024Article
- Brain tumor segmentation based on the U-NET++ network with efficientnet encoder.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Highly accurate brain tumor detection with high sensitivity using transform-based functions and machine learning algorithms.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.Scientific data · 2022Article
- An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation.Applied soft computing · 2021Article
- Article
- Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images.Scientific reports · 2020Article
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
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Authors and funding
6 authors at 3 institutions in 1 country.
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Abstract
This work proposes a novel framework for brain tumor segmentation prediction in longitudinal multi-modal MRI scans, comprising two methods; feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density feature to obtain tumor segmentation predictions in follow-up timepoints using data from baseline pre-operative timepoint. The cell density feature is obtained by solving the 3D reaction-diffusion equation for biophysical tumor growth modelling using the Lattice-Boltzmann method. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method, known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). We quantitatively evaluate both proposed methods using the Dice Similarity Coefficient (DSC) in longitudinal scans of 9 patients from the public BraTS 2015 multi-institutional dataset. The evaluation results for the feature-based fusion method show improved tumor segmentation prediction for the whole tumor(
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