Multi-organ localization with cascaded global-to-local regression and shape prior
Résumé
We propose a method for fast, accurate and robust localization of several organs in medical
images. We generalize global-to-local cascade of regression random forest to multiple organs. A first
regressor encodes global relationships between organs, learning simultaneously all organs parameters.
Then 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 propose an extensive
study of the different learning and testing parameters, showing both their robustness to medium
variations and their influence on the final algorithm accuracy.Finally we demonstrate the robustness
and accuracy of our approach by evaluating the localization of six abdominal organs (liver, two
kidneys, spleen, gallbladder and stomach) on a large and diverse database of 130 CT volumes.
Moreover, the comparison of our results with two existing methods shows significant improvements
brought by our approach and our deep understanding and optimization of the parameters.