Titlebook: Information Processing in Medical Imaging; 22nd International C Gábor Székely,Horst K. Hahn Conference proceedings 2011 Springer-Verlag Gmb

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Segmentation of Brain Images Using Adaptive Atlases with Application to Ventriculomegaly when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.

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Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learrsion of the Jensen-Shannon divergence. Our algorithm is assessed on a database of 196 labeled clinical CT scans with high variability in resolution, anatomy, pathologies, etc. Quantitative evaluation shows that, compared with randomly selecting the scans to annotate, our method decreases the number

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Surface–Region Context in Optimal Multi-object Graph-Based Segmentation: Robust Delineation of Pulmoional graph-cut method, our new approach provided significantly better results (. < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76±0.10) was improved to 0.84±0.05 when employing our new method for pulmonary tumor segmentation.

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On the Extraction of Topologically Correct Thickness Measurements Using Khalimsky’s Cubic Complexwith an ordered upwind differencing method using an anisotropic grid with the probabilistic segmentation as a speed function. Experiments performed on digital phantoms show that the proposed method obtains topologically correct thickness measurements with an increase in accuracy when compared to two

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