Liver segmentation using location and intensity probabilistic atlases
| Autor: | Shadrokh Samavi, Kevin R. Ward, Kayvan Najarian, Samuel Habbo-Gavin, David Fessell, Negar Farzaneh, S. M. Reza Soroushmehr, Hirenkumar Patel |
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| Rok vydání: | 2017 |
| Předmět: |
Male
Computer science education 0206 medical engineering Computed tomography 02 engineering and technology computer.software_genre Liver segmentation 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Similarity (network science) Voxel medicine Image Processing Computer-Assisted Humans Computer vision Reliability (statistics) Models Statistical medicine.diagnostic_test business.industry Probabilistic logic Reproducibility of Results Image segmentation 020601 biomedical engineering Intensity (physics) Visual inspection Liver Female Artificial intelligence business Tomography X-Ray Computed computer Algorithms |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | In a variety of injuries and illnesses, internal organs in the abdominal and pelvic regions, in particular liver, may be compromised. In the current practice, CT scans of liver are visually inspected to investigate the integrity of the organ. However, the size and complexity of the CT images limits the reliability of visual inspection to accurately assess the health of liver. Computer-aided image analysis can create fast and quantitative assessment of liver from the CT, in particular in the environments where access to skilled radiologists may be limited. In this paper we propose a hierarchical method based on probabilistic models of position and intensity of voxels for automated segmentation of liver that achieves the Dice similarity coefficient of higher than 89%. |
| Databáze: | OpenAIRE |
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