Learning Ensemble of Local PDM-based Regressions. Yen Le Computational Biomedicine Lab Advisor: Prof. Ioannis A. Kakadiaris

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1 Learnng Ensemble of Local PDM-based Regressons Yen Le Computatonal Bomedcne Lab Advsor: Prof. Ioanns A. Kakadars 1

2 Problem statement Fttng a statstcal shape model (PDM) for mage segmentaton Callosum segmentaton n MRI mages Segmentaton of lungs, heart and clavcles (B. Gnneken et al., Meda 2006) 2

3 Pont Dstrbuton Models (PDMs) 1. A shape s defned by a set of ponts. x = [ x, y, x, y,..., x, y ] T m m 3. Compute a PDM: ( xp, ) 2. Data matrx of tranng shapes (after global algnment) X [ x x... x ] n 2m = 1 2 n T x 1 n x n = 1 = Mean shape [ ] P =Φ1 Φ2... Φd man axes of the cloud of tranng shapes (frst egenvectors of covarance matrx of X) 3

4 Pont Dstrbuton Models (2) 4. Shape representaton n the PDM: Pb 5. Fttng a PDM to a shape n the orgnal space: * 2 x = argmn x W[ x ( x+ Pb)] 2 W = dag( w, w,..., w, w,..., w, w ) w 1 1 : weght of model pont m m p 4

5 Actve Shape Model (ASM) A shape model fttng approach usng PDM constrants In$alze the shape Compute new shape es$mate Ft the PDM to the shape es$mate Local best match for a model pont Model pont One teraton of ASM (Fgure from Statstcal shape models for 3D medcal mage segmentaton: a revew, T. Hemann and H-P. Menzer, MEDIA 2009) 5

6 Segmentaton of mouse gene expresson mages Impact Tens thousands of genes Keys to dscoverng gene functons and gene networks 6

7 Challenges Complex appearance Hppocampus regon (blue boundares) n three example mages Hgh varaton n shape The frst (L) and second (R) largest ASM modes of the shape model of mouse bran mages. 7

8 ASMs Man lmtatons Inablty to represent a large range of varatons of a complex shape Inablty to account for large errors n detecton of model ponts Improvements To mprove the representaton ablty of shape models Introduce devatons that are not explaned n the tranng data Partton the shape model nto local sub-shape models Use dstance-based weghts to ft each model ponts locally To account for the large errors n detecton of model ponts Use sparse shape model Weght model ponts based on reconstructon resduals Detect and correct outlers Use bnary weghts computed based on the ntalzed shape 8

9 PDM-ENLOR: Our Approach PDM-ENLOR: PDM-based ENsemble of LOcal Regressons p Smple models + Good gudance for fttng Some ponts can be detected relably (salent ponts) A model pont can be nferred from locatons of the salent ponts (reference ponts) 9

10 PDM-ENLOR: Our Approach (2) p Smple models + Good gudance for fttng Use multple local-to-global models to generate probable canddates o Local models can provde mproved geometrc constrants o Global models s less senstve to non-robust detecton of model ponts The probable canddates are used to obtan the target shape pont 10

11 PDM-ENLOR: Learnng ensemble coeffcents Canddates proposed by local models for p n tranng mages A x x x y y y =... x x x y y y *(1,1) *(2,1) *( k,1) *(1,1) *(2,1) *( k,1) *(1, n) *(2, n) *( k, n) *(1, n) *(2, n) *( k, n) p Annota$ons of p n tranng mages g = x, y, x, y,, x, y ( g,1) ( g,1) ( g,2) ( g,2) ( g, n) ( g, n) T Coeffcent vector for model pont p : * 2 argmnc j c = Ac g, s.t. c 0 11

12 PDM-ENLOR: Framework Tranng data TRAINING PHASE Reference pont selecton Regresson model constructon Regresson model tranng Ensemble coeffcent learnng PDMs Coeffcents Test mage TESTING PHASE Reference pont detecton Shape model pont regresson Model combnaton Ftted shape model Poston of a model pont p k = cj f j Rj j= 1 ( ) c j Ensemble coeffcents; f j Regresson models; R j Reference pont sets 12

13 Results A comparson of the mean and confdence-nterval of the overlap scores computed from the results of ASM-SIM (global fttng), Amberg-SIM (local fttng) and PDM-ENLOR. 13

14 Results (2) The manually annotated boundares (red) and the resultng boundares (blue) of automatc segmentaton on mages of genes Ttc3 (top) and Neurog2 (bottom). 14

15 Name of Advsor: Prof. Ioanns A. Kakadars Area of Research: Computer Vson Frst semester at the UHCS PhD Program: Fall 2008 Date of PhD proposal: Aprl, 2013 Date of planned defense: Aprl, 2014 PhD Dssertaton Commttee Members Dr. Ioanns A. Kakadars (UH, Department of Computer Scence) Dr. James P. Carson (Unversty of Texas n Austn) Dr. Zhgang Deng (UH, Department of Computer Scence) Dr. Chrstoph Eck (UH, Department of Computer Scence) Dr. Tao Ju (Washngton Unversty n St. Lous) Dr. Uday Kurkure Dr. Shshr Shah (UH, Department of Computer Scence) Internshp: Google Inc.,

16 Publcatons Refereed Conference Publcatons Y.H. Le, M. Bondesson, N. Ducharme, and I.A. Kakadars, "A framework for buldng mult-tssue atlas of zebrafsh embryo," n Proc. IEEE ISBI 2014 (In Press). Y.H. Le, U. Kurkure, I.A. Kakadars. PDM-ENLOR: Learnng ensemble of local PDM-based regressons. In Proc. IEEE CVPR 2013, pp Acceptance Rate: 25.2%. Y.H. Le, U. Kurkure, N. Paragos, T. Ju, J.P. Carson, I.A. Kakadars. Smlarty-based appearance-pror for ttng a subdvson mesh n gene expresson mages. In Proc. MICCAI 2012, pp Acceptance Rate: 32%. U. Kurkure, Y.H. Le, N. Paragos, T. Ju, J.P. Carson, I.A. Kakadars. Markov Random Feld-based fttng of a subdvson-based geometrc atlas. In Proc. ICCV 2011, pp Acceptance Rate: 24%. U. Kurkure, Y.H. Le, N. Paragos, J.P. Carson, T. Ju, I.A. Kakadars. Landmark/mage-based deformable regstraton of gene expresson data. In Proc. IEEE CVPR 2011, pp Acceptance Rate: 26.4%. Refereed Journal Publcaton U. Kurkure, D.R. Chttajallu, G. Brunner, Y.H. Le, I.A. Kakadars. A supervsed classfcaton-based method for coronary calcum detecton n non-contrast CT. The Internatonal Journal of Cardovascular Imagng 2010, vol. 26, no. 7, pp Impact Factor: Refereed Journal Submssons Y.H. Le, U. Kurkure, I. A. Kakadars. 3D Dense Local Pont Descrptors for Mouse Bran Gene Expresson Images. Computerzed Medcal Imagng and Graphcs, Impact Factor: Y.H. Le, U. Kurkure, I.A. Kakadars. PDM-ENLOR: Learnng ensemble of local PDM-based regressons. Medcal Image Analyss, Impact Factor: U. Kurkure, Y.H. Le, I.A. Kakadars. Landmark/mage-based deformable regstraton of gene expresson data. Journal of Bomedcal and Health Informatcs. Impact factor: