The isometric log-ratio transform for probabilistic multi-label anatomical shape representation.

Sources of uncertainty in the boundaries of structures in medical images have motivated the use of probabilistic labels in segmentation applications. An important component in many medical image segmentation tasks is the use of a shape model, often generated by applying statistical techniques to training data. Standard statistical techniques (e.

MRI-based 3D shape analysis of thigh muscles patients with chronic obstructive pulmonary disease versus healthy adults.

Rationale And Objective: Because lower limb muscles differ in architecture and function, the systemic effects of chronic obstructive pulmonary disease (COPD) and related disuse may result in regional abnormalities. The purpose of this study was to investigate the differences between patients with COPD and healthy controls in three-dimensional shape and size measurements of individual thigh muscles.

Materials And Methods: Twenty patients with COPD and 20 healthy adults (aged 55-79 years) underwent magnetic resonance imaging of the thighs.

Probabilistic multi-shape representation using an isometric log-ratio mapping.

Several sources of uncertainties in shape boundaries in medical images have motivated the use of probabilistic labeling approaches. Although it is well-known that the sample space for the probabilistic representation of a pixel is the unit simplex, standard techniques of statistical shape analysis (e.g.

Extraction of the plane of minimal cross-sectional area of the corpus callosum using template-driven segmentation.

Changes in corpus callosum (CC) size are typically quantified in clinical studies by measuring the CC cross-sectional area on a midsagittal plane. We propose an alternative measurement plane based on the role of the CC as a bottleneck structure in determining the rate of interhemispheric neural transmission. We designate this plane as the Minimum Corpus Callosum Area Plane (MCCAP), which captures the cross section of the CC that best represents an upper bound on interhemispheric transmission.