A flexible framework for medical image segmentation has been developed. The semi-automatic method effectively segments imaging data volumes through the use of 3D region growing guided by initial seed points. Seed voxels may be specified interactively with a mouse or through the selection of intensity thresholds. Segmentation proceeds automatically following seed selection on only a few slices in the volume due to the 3D nature of the region growth. Computational efficiency is realized by utilizing fast data structures. The 3D region growing algorithm has been used for a variety of segmentation tasks. Magnetic resonance (MR) brain volumes acquired at all three imaging orientations have been accurately segmented. The method also was applied to clinical short-axis cardiac data sets for the extraction of the endocardial blood pool. Additionally, preliminary results indicate that myocardial infarcts from high resolution MR images of formalin-fixed hearts may be segmented using our region growing approach. The algorithm is not confined to a particular imaging modality or orientation. It makes use of information in the third dimension, resulting in increased accuracy. Moreover, the entire method can be implemented in a short amount of time due to its simplicity.