ArticleProceedings of SPIE--the International Society for Optical Engineering2017
Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network.Scientific reports · 2022Article
- Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images.Scientific reports · 2020Article
- A Fully Automated Deep Learning Network for Brain Tumor Segmentation.Tomography (Ann Arbor, Mich.) · 2020Article
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5 authors.
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Abstract
In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.
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