Razmig Kéchichian

TL;DR: A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks.

TL;DR: A web-accessible image visualization and processing framework well-suited for medical applications that allows the end-user to efficiently browse and visualize volumic images in an Out-Of-Core (OOC) manner, annotate and apply server-side image processing algorithms and interactively visualize 3D medical models.

TL;DR: Qualitative and quantitative analyses and comparison with a Potts prior-based approach and a previous contribution show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution.

TL;DR: A generic method for automatic multiple-organ segmentation based on a multilabel Graph Cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them is proposed.

TL;DR: Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the state-of-the-art confirm that the approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance.