Smart Brush: A real time segmentation tool for 3D medical images

Level-set methods have proven to be powerful and flexible for computer vision and medical imaging applications. Unfortunately, their drawback is the high computational effort required, which hampers the applicability to imaging tools for real clinical use. A new interactive 3D segmentation tool is presented, addressing operational speed issues by computing level-set surface models interactively. Real time segmentation is achieved by implementing a sparse field level-set solver on the Graphics Processing Unit. The tool enables users - even non-expert - to produce good, reliable segmentations in a short time and with few training. A validation study is ongoing addressing wrist bone segmentations in magnetic resonance images of patients affected by Rheumatoid Arthritis. Preliminary positive results of qualitative and quantitative validation are presented and discussed at the end of the paper.

[1]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[2]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[3]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[4]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Ross T. Whitaker,et al.  GIST: an interactive, GPU-based level set segmentation tool for 3D medical images , 2004, Medical Image Anal..

[6]  Lorenzo Cesario,et al.  AB1257 Assessing MRI erosions in the rheumatoid wrist: A comparison between RAMRIS and a semiautomated segmentation software , 2013 .

[7]  Ross T. Whitaker,et al.  Volumetric deformable models: active blobs , 1994, Other Conferences.

[8]  J. Sethian,et al.  A Fast Level Set Method for Propagating Interfaces , 1995 .

[9]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[10]  Joseph Ross Mitchell,et al.  A work-efficient GPU algorithm for level set segmentation , 2010, HPG '10.

[11]  Zubin C. Bhaidasna,et al.  A Review on Level Set Method for Image Segmentation , 2013 .

[12]  Michela Spagnuolo,et al.  Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications , 2012, MCBR-CDS.

[13]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[14]  Martin Rumpf,et al.  Level set segmentation in graphics hardware , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[15]  Ross T. Whitaker,et al.  A Level-Set Approach to 3D Reconstruction from Range Data , 1998, International Journal of Computer Vision.

[16]  Jie Cheng,et al.  Programming Massively Parallel Processors. A Hands-on Approach , 2010, Scalable Comput. Pract. Exp..

[17]  J. Sethian,et al.  FRONTS PROPAGATING WITH CURVATURE DEPENDENT SPEED: ALGORITHMS BASED ON HAMILTON-JACOB1 FORMULATIONS , 2003 .

[18]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[19]  Ross T. Whitaker,et al.  A streaming narrow-band algorithm: interactive computation and visualization of level sets , 2004, IEEE Transactions on Visualization and Computer Graphics.

[20]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[21]  Olivier Bernard,et al.  A rigorous and efficient GPU implementation of level-set sparse field algorithm , 2012, 2012 19th IEEE International Conference on Image Processing.

[22]  Ross T. Whitaker,et al.  Interactive, GPU-Based Level Sets for 3D Segmentation , 2003, MICCAI.