Fast 4D segmentation of large datasets using graph cuts

In this paper, we propose to use 4D graph cuts for the segmentation of large spatio-temporal (4D) datasets. Indeed, as 4D datasets grow in popularity in many clinical areas, so will the demand for efficient general segmentation algorithms. The graph cuts method has become a leading method for complex 2D and 3D image segmentation in many applications. Despite a few attempts in 4D, the use of graph cuts on typical medical volume quickly exceeds today’s computer capacities. Among all existing graph cuts based methods the multilevel banded graph cuts is the fastest and uses the least amount of memory. Nevertheless, this method has its limitation. Memory becomes an issue when using large 4D volume sequences, and small structures become hardly recoverable when using narrow bands. We thus improve the boundary refinement efficiency by using a 4D competitive region growing. First, we construct a coarse graph at a low resolution with strong temporal links to prevent the shrink bias inherent to the graph cuts method. Second, we use a competitive region growing using a priority queue to capture all fine details. Leaks are prevented by constraining the competitive region growing within a banded region and by adding a viscosity term. This strategy yields results comparable to the multilevel banded graph cuts but is faster and allows its application to large 4D datasets. We applied our method on both cardiac 4D MRI and 4D CT datasets with promising results.