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We present a new method for the segmentation of multiple organs (2D or 3D) which enables user inputs for smart contour editing. By extending the work of [1] with user-provided hard constraints that can be optimized globally or locally, we propose an efficient and user-friendly solution that ensures consistent feedback to the user interactions. We demonstrate the potential of our approach through a user study with 10 medical imaging experts, aiming at the correction of 4 organ segmentations in 10 CT volumes. We provide quantitative and qualitative analysis of the users’ feedback.

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.