A tumor and liver automatic segmentation challenge
Authors/Creators
- 1. University of Burgundy / ImViA laboratory, Dijon, France
- 2. Institute of High Performance Computing (IHPC), A*STAR, Singapore
- 3. University hospital of Dijon / ImViA laboratory, Dijon, France
- 4. University of Burgundy / Centre Georges François Leclerc ImViA laboratory
- 5. University of Burgundy / Centre Georges François Leclerc / ImViA
- 6. University Hospital of Dijon, Dijon, France
Description
Liver cancer is the sixth most common cancer in the world but the second leading cause of cancer mortality in men. In the case of Selective Internal Radiation Therapy (SIRT) treatment, a contrast-enhanced MRI exam is usually performed beforehand to visualize and delineate the tumor and the whole liver. In clinical practice, delineations are performed either manually or semi-automatically, but are time-consuming prone to intra- and interoperator variabilities. The objective of this challenge is to develop algorithms to automate the delineation of the liver and tumor volumes on contrast-enhanced MRI images. These fully automatic algorithms should improve SIRT treatment planning and therefore treatment delivery and ultimately patient response rate and survival time.
Files
Atumorandliverautomaticsegmentationchallenge_04-14-2023_12-53-52.pdf
Files
(2.7 MB)
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