MRI images have been used for a wide variety of medical applications for a long time because they are safe and highly sensitive at detecting of tissue abnormalities that indicate cancer. MRI is generated by measuring the response of tissue components to a magnetic field. Also, based on the published statistics, brain tumor is one of the most common causes of death and early detection and monitoring is crucial for treatment. Literature and market review suggests that although extensive research exists on brain tumor detection using MRI images, MRI?based systems designed for brain tumor detection that have ultimate clinical value and use are lacking. Accordingly, we propose a new software technology that effectively detect, segment, classify and monitor brain tumor in MRI images.

Public Health Relevance

There have been extensive efforts on brain tumor detection in MR images because early detection has been always crucial for success of treatment. For ongoing work in our parent RO1 grant we are developing new machine learning and image analysis methods that for automatic detection and segmentation of brain tumor in MRI images, which is expected to help patients to have a much better chance of successful treatment. If successful, the proposed software technology in this project may potentially be the first that may be commercialized for brain tumor segmentation and classification on MRI images.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Duan, Qi
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Old Dominion University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
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Vidyaratne, L; Alam, M; Shboul, Z et al. (2018) Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation. Proc SPIE Int Soc Opt Eng 2018:
Shboul, Zeina; Vidyaratne, Lasitha; Alam, Mahbubul et al. (2018) Glioblastoma and Survival Prediction. Brainlesion (2017) 10670:358-368
Shboul, Zeina A; Reza, Sayed M S; Iftekharuddin, Khan M (2018) Quantitative MR Image Analysis for Brian Tumor. VipIMAGE 2017 (2017) 27:10-18
Pei, Linmin; Reza, Syed M S; Li, Wei et al. (2017) Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proc SPIE Int Soc Opt Eng 10134:
Maier, Oskar; Menze, Bjoern H; von der Gablentz, Janina et al. (2017) ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 35:250-269
Reza, Syed M S; Iftekharuddin, Khan M (2016) Glioma Grading Using Cell Nuclei Morphologic Features in Digital Pathology Images. Proc SPIE Int Soc Opt Eng 9785:
Bron, Esther E; Smits, Marion; van der Flier, Wiesje M et al. (2015) Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111:562-79
Reza, Syed M S; Mays, Randall; Iftekharuddin, Khan M (2015) Multi-fractal Detrended Texture Feature for Brain Tumor Classification. Proc SPIE Int Soc Opt Eng 9414: