Glioma brain tumor detection using dual convolutional neural networks and histogram density segmentation algorithm

Sarala, B.; Sumathy, G.; Kalpana, A.V.; Hephzipah, J. Jasmine

doi:10.1016/j.bspc.2023.104859

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2024

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Cited by 19 publications

References 11 publications

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Brain Tumor MRI Segmentation Using Deep Instance Segmentation with Bioinspired Optimization Algorithm

Balaji,

Alsid,

Mishra

et al. 2024

Lecture Notes in Networks and Systems

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Brain Tumor MRI Segmentation Using Deep Instance Segmentation with Bioinspired Optimization Algorithm

Balaji,

Alsid,

Mishra

et al. 2024

Lecture Notes in Networks and Systems

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AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis

Lee,

Kim,

Choi

et al. 2024

J Digit Imaging. Inform. med.

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TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging.

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Dual path sub-type diagnostic model based on the anatomic morphological feature

Fang,

Jiang

2024

Biomedical Signal Processing and Control

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Glioma brain tumor detection using dual convolutional neural networks and histogram density segmentation algorithm

Cited by 19 publications

References 11 publications

Brain Tumor MRI Segmentation Using Deep Instance Segmentation with Bioinspired Optimization Algorithm

Brain Tumor MRI Segmentation Using Deep Instance Segmentation with Bioinspired Optimization Algorithm

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis

Dual path sub-type diagnostic model based on the anatomic morphological feature

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