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In the last decade, supervoxels have become a useful mid-level representation of volumetric medical images such as MRIs and CT scans. Several methods were suggested to produce uniform supervoxels, yet little has been done to generate content-sensitive over-segmentations. This is particularly beneficial to 3D medical image analysis, where sizes of anatomical structures vary largely. In this paper, we propose AdaSLIC as an adaptive supervoxel generation technique that applies to volumetric medical images. In small structures, it generates tiny supervoxels to capture the details of the image. Meanwhile, it partitions large structures into bigger supervoxels, hence leading to a sparse description. The proposed technique is an extension of the Simple Linear Iterative Clustering (SLIC) algorithm. Rather than using a regular sampling to initiate supervoxel centers, a content-sensitive initialization is performed …