Multi-organ localization combining global-to-local regression and confidence maps
Résumé
We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize global-to-local
cascades of regression forests [1] to multiple organs. A first regressor encodes global relationships between organs. Subsequent regressors refine
the localization of each organ locally and independently for improved
accuracy. We introduce confidence maps, which incorporate information
about both the regression vote distribution and the organ shape through
probabilistic atlases. They are used within the cascade itself, to better
select the test voxels for the second set of regressors, and to provide richer
information than the classical bounding boxes thanks to the shape prior.
We demonstrate the robustness and accuracy of our approach through a
quantitative evaluation on a large database of 130 CT volumes.