Interactive, GPU-Based Level Sets for 3D Segmentation  Aaron E. Lefohn, Joshua E. Cates and Ross T. Whitaker

 

Tumor Segmentation using our system	Expert hand segmentation

Abstract

    While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute.  Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications.  The second problem is compounded by the first.  This paper presents a tool for 3D segmentation that relies on level-set surface models computed at interactive rates on commodity graphics cards (GPUs).  The mapping of a level-set solver to a GPU relies on a novel mechanism for GPU memory management.  The interactive rates for solving the level-set PDE give the user immediate feedback on the parameter settings, and thus users can tune three separate parameters and control the shape of the model in real time.  We have found that this interactivity enables users to produce good, reliable segmentations.  This paper presents qualitative and quantitative results from this tool on brain tumor segmentation to support this observation.

Authors

Aaron Lefohn	University of Utah
Joshua Cates	University of Utah
Ross Whitaker	University of Utah

Paper

"Interactive, GPU-Based Level Sets for 3D Segmentation" in Medical Image Computing and Computer Assisted Intervention" (MICCAI) 2003.	pdf
Detailed description of the GPU-based sparse-grid level-set solver.	pdf
University of Utah School of Computing Technical Report	pdf	ps

Talks

MICCAI 2003 Presentation

ppt

Movies

A user segmenting a brain tumor from a 256 x 256 x 124 MRI	mpeg	quicktime
A user segmenting the cerebral cortex from a 128 x 128 x 88 MRI	quicktime