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Technical Program
Paper Detail
| Paper: | MA-L3.6 |
| Session: | Biomedical Image Segmentation |
| Time: | Monday, October 9, 11:40 - 12:00 |
| Presentation: |
Lecture
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| Title: |
AUTOMATIC HOT SPOT DETECTION AND SEGMENTATION IN WHOLE BODY FDG-PET IMAGES |
| Authors: |
Haiying Guan; University of California, Santa Barbara | | | | | Toshiro Kubota; Siemens Medical Solutions | | | | | Xiaolei Huang; Siemens Medical Solutions | | | | | Xiang Sean Zhou; Siemens Medical Solutions | | | | | Matthew Turk; University of California, Santa Barbara | | |
| Abstract: |
We present a system for automatic hot spots detection and segmentation in whole body FDG-PET images. The main contribution of our system is threefold. First, it has a novel body-section labeling module based on spatial Hidden-Markov Models (HMM); this allows different processing policies to be applied in different body sections. Second, the Competition Diffusion (CD) segmentation algorithm, which takes into account body-section information, converts the binary thresholding results to probabilistic interpretation and detects hot-spot region candidates. Third, a recursive intensity mode-seeking algorithm finds hot spot centers efficiently, and given these centers, a clinically meaningful protocol is proposed to accurately quantify hot spot volumes. Experimental results show that our system works robustly despite the large variations in clinical PET images. |
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