Science Score: 49.0%
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Low similarity (7.8%) to scientific vocabulary
Repository
FastCleverSegMethod
Basic Info
- Host: GitHub
- Owner: JonathanRamos
- License: gpl-3.0
- Language: C++
- Default Branch: main
- Size: 1.94 GB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
FastCleverSeg
Since the manual segmentation is time-consuming and impractical to perform for a large number of exams, semi-automatic segmentation tools minimize the labor-intensiveness by using minimal user input. We propose FastCleverSeg, in which the experimental evaluation with several MRI databases from distinct hospitals showed that our approach can considerably speed up the manual annotation process with a Dice Score of 94\% and the fastest processing time ($25 \pm 30$ ms). Therefore, our approaches can aid physicians in producing reliable ground-truths in a fast manner, speeding up the laborious task of manually segmenting regions of interest.
If you use any part of this repository, please cite:
Jonathan S. Ramoset al., Fast and accurate 3-D spine MRI segmentation using FastCleverSeg,
Magnetic Resonance Imaging, Volume 109, 2024, Pages 134-146, ISSN 0730-725X,
https://doi.org/10.1016/j.mri.2024.03.021.
(https://www.sciencedirect.com/science/article/pii/S0730725X24000778)
We made avaiable all our codes, datasets and results as follows: - Algorithms and codes - Image Datasets - Results
Results
Results considering each anatomical group available at: Muscles, Discs, and Vertebrae.
Here, we present the overall results as follows:
Results Without EANIS
Figure 2: Example of annotations.
Figure 3: Overall results comparison
Figure 4: Segmentation Results Comparison.
Owner
- Name: Jonathan Ramos
- Login: JonathanRamos
- Kind: user
- Repositories: 6
- Profile: https://github.com/JonathanRamos
PhD Student - University of São Paulo - USP