1. Global localization of 3D anatomical structures by pre-filtered Hough Forests and discrete optimization Speaker: Longwei Fang Date: 8/July,2016

2. Outline Background Methods Experiments Results Conclusion and Discussion

3. Background Application Spinal flexibility [1], knee alignment angles [2], fracture detection in osteoporosis diagnosis [3] State of the art Harris corners + graph matching [4], learned based + graph model [5], classification + mean-shift clustering [6] Limitations Local search, fixed graph structure, numbers of accurate candidates Proposal Classification + regression + graphical model [1]Lamarre et al., 2009; [2]Issa et al., 2007; [3]Roberts et al. 2006; [4]Donner et al., 2007a; [5]Bergtholdt et al., 2010; [6]Shotton et al., 2011

4. Methods Fig. 1. Outline of the proposed anatomical structure localization approach

5. Methods Random forest classification Positive sample For each v, build a feature vector Train random forest as n=32 as an L+1 class classifier Particle Hough Forests (probability aggregation) For each l, take voxels training Hough regression During localization For each candidate gets accumulated probability

6. Methods Model localization via graph matching Construct a Markov Random Field Graph topology landmarks s, t and their coordinates d largest weights p(c l )

7. Experiments 3 Data sets X-ray handCT handCT whole body

8. Results

9. Conclusion and Discussion Random forest + Hough regression + MRF Learned graph topology + small number of candidates High accuracy + Fast Sufficient experiments and comparison