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Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation
[chapter]
2017
Lecture Notes in Computer Science
This article presents an efficient method for weakly-supervised organ segmentation. It consists in over-segmenting the images into objectlike supervoxels. A single joint forest classifier is then trained on all the images, where (a) the supervoxel indices are used as labels for the voxels, (b) a joint node optimisation is done using training samples from all the images, and (c) in each leaf node, a distinct posterior distribution is stored per image. The result is a forest with a shared
doi:10.1007/978-3-319-67389-9_10
fatcat:ij6tdpd3pvfxxjvnsuf66d7tiy