Titlebook: Medical Computer Vision: Algorithms for Big Data; International Worksh Bjoern Menze,Georg Langs,Dimitris Metaxas Conference proceedings 201

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Creating a Large-Scale Silver Corpus from Multiple Algorithmic Segmentationsstructures automatically and interactively. While they provide segmentations on a much larger scale than possible to achieve with expert annotators, they are typically less accurate than experts. We present and compare approaches to estimate segmentations on large imaging data sets based on a small

opportune

ICLE

Joint Feature-Sample Selection and Robust Classification for Parkinson’s Disease Diagnosischemical messenger in the brain impairs several brain regions and yields to various movement and non-motor symptoms. The incidence of PD is considered to be doubled in the next two decades and this urges more researches on its early diagnosis and treatment. In this paper, we propose an approach to d

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TIA742

Psoas Major Muscle Segmentation Using Higher-Order Shape Priorground truth data prepared by a clinical expert, we validated the method where it achieved the Jaccard similarity coefficient (JSC) of 75.4 % (right major) and 77.5 % (left major). We also confirmed that the proposed method worked well for thick CT images.

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Dynamic Tree-Based Large-Deformation Image Registration for Multi-atlas Segmentationints. A dynamic tree capturing the structural relationships between images is then used to further reduce misalignment errors. Validation on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy.