• 3 Challenges: MS lesions, Liver tumors, Coronary arteries

• Training and evaluation data, quantitative evaluation

• 36 teams submitted results (120 registered)

Segmentation Workshop

• Organized by Wiro Niessen, Theo van Walsum, Coert Metz, Michiel Schaap (Erasmus, Rotterdam NL)

• 32 datasets with ground truth by experts (with variability)

• 8 training

• 16 pre-workshop testing

• 8 on-site testing

• Quantitative criteria (overlap and accuracy)

• 3 categories: fully auto (5), minimal inter. (3), interactive (5)

Coronary Challenge

• Winners:

• fully auto: Zambal et. al. (VrVis, Austria)

• min. interactive: Dikici and O’Donnell (SCR)

• interactive: Friman et. al. (Mevis Lab, Germany)

• Siemens’ team 2 (Tek et. al.): 2nd of fully auto, 3rd overall

Coronary Challenge

• Zambal et. al.

• heart model optimized by diffeomorphisms

• sampling feature with histogram separability measure

Coronary Challenge

• Friman et. al. (cf. ISBI’08)

• template model optimization

• multi-hypothesis deterministic tracking

Coronary Challenge

• Dikici and O’Donnell

• axis symmetry voting + graph-cuts

Coronary Challenge

• Bauer and Bischof (Graz, Austria)

• GVF-based medialness

Coronary Challenge

• Keynote: John Condeelis (A. Einstein College, Yeshiva U., NY)

“High resolution optical in vivo imaging of tumor cell mobility, chemotaxis, invasion and metastasis in breast tumors”

•Multi-photon imag.

•Cell tagging•Interactions

• Effect of graph topology (neighborhoods)

• 6-c, 26-c, 10-c

• Effect of weighting function

• Graph algorithms: graph-cut and random walker

• Tests on 62 CT datasets (lymph nodes and tumor mainly)

• Results: topology

• increased connectivity stabilizes parameter sensitivity

• isotropic neighborhoods (6-, 26-c) more stable than anisotropic ones

• Results: weighting

• histogram-based best, but large deviation (app.-dependent)

• reciprocal weighting consistently more stable than Gaussian

• better absolute performance

• lower std. deviation

• significantly lower parameter sensitivity

• T1 MR (tested on IBSR Harvard)

• Tree dependencies between structuresVentricles

Caudate Nuclei

Putamina Thalami

• Markovian dependencies from manual binary segmentations

• Model: 1 binary segmentation

• Auto-context model

• iterative training of classifiers (Adaboost and PBTs)

• start with image patches -> probability maps

• training set is augmented with these maps

• training is iterated

• Proof of convergence (error decreasing)

• Rely on Adaboost for discrimination

• Simultaneous co-registration and construction of image templates from a set of images

• Gaussian mixture model

• Optimization by Generalized E.M. (B-Spline registration)

• Similar (identical?) to S. Allassonniere’s model (Poly.)

• Various optimizations: stochastic sampling, registration “anchoring”, etc...

• Notion of “stability” of the templates

• Experiment on the OASIS database (416 brain MR)(age and Alzheimer)

K=2

K=3

“Some brains age faster than others”

• Dementia experiment (60 datasets from OASIS)

“Dementia not a binary state”

• Texture models for Active Shape Models

• Comparison with Active Appearance Models (AAM, Cootes)

• Application to heart, brain ventricles, vertebraes

• Idea: model uncertainty through entropy

• Optimize intensity mappings to s levels (normalization)

• Competition between model specificity and image information

• Optimization of the model by simulated annealing

• Analogy with MDL

• Optimization of unseen pictures

• iterative max a posteriori

• normalization (mapping) by voting by the models (max likelihood)

• prior from the ASM

• exhaustive search on geometric parameters