ArticleJournal of medical physics2011
Development of image-processing software for automatic segmentation of brain tumors in MR images.
Article in Journal of medical physics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- MRI-based brain tumor segmentation using FPGA-accelerated neural network.BMC bioinformatics · 2021Article
- Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features.Frontiers in neuroscience · 2019Article
- Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.Journal of digital imaging · 2015Article
- MRI segmentation of the human brain: challenges, methods, and applications.Computational and mathematical methods in medicine · 2015Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called 'Prometheus,' which performs neural system-based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.