| Paper: | WA-L1.7 |
| Session: | Image Processing and Analysis for Oncology |
| Time: | Wednesday, September 19, 12:10 - 12:30 |
| Presentation: |
Special Session Lecture
|
| Title: |
3D SEGMENTATION OF THE PROSTATE VIA POISSON INVERSE GRADIENT INITIALIZATION |
| Authors: |
Bing Li; University of Virginia | | |
| | Abhay V. Patil; University of Virginia | | |
| | John A. Hossack; University of Virginia | | |
| | Scott T. Acton; University of Virginia | | |
| Abstract: |
Accurate segmentation and volumetric assessment of the enlarged prostate is critical to assessment of cancer progression. Moreover, 3D segmentation is necessary for treatment in both radiotherapy and brachytherapy. We propose a 3D segmentation solution for ultrasound images of the prostate based on deformable surfaces. The deformable surfaces are propelled by the vector field convolution (VFC) external force model. This external force has both computational efficiency and solution quality advantages over existing techniques such as gradient vector flow (GVF). A salient aspect of the segmentation solution proposed here is the ability to automatically initialize the deformable surface in 3D. The initialization method exploits a novel Poisson inverse gradient technique that essentially solves the inverse problem from the external force field to the external energy and determines the most likely coarse segmentation. We validate our 3D segmentation on simulated images of the prostate. Furthermore, simulated data show that PIG initialization leads to a 60% reduction in segmentation error for high curvature contours. |
