A Work-Efficient GPU Algorithm for Level Set Segmentation

Abstract

We present a novel GPU level set segmentation algorithm that is both work-efficient and step-efficient. Our algorithm: (1) has linear work-complexity and logarithmic step-complexity, both of which depend only on the size of the active computational domain and do not depend on the size of the level set field; (2) limits the active computational domain to the minimal set of changing elements by examining both the temporal and spatial derivatives of the level set field; (3) tracks the active computational domain at the granularity of individual level set field elements instead of tiles without performance penalty; and (4) employs a novel parallel method for removing duplicate elements from unsorted data streams in a constant number of steps. We apply our algorithm to 3D medical images and we demonstrate that in typical clinical scenarios, our algorithm reduces the total number of processed level set field elements by 16x and is 14x faster than previous GPU algorithms with no reduction in segmentation accuracy.