Assisted breech delivery with transverse vaginal septum.

The incidence of vaginal septum is rare. The infrequency of this anomaly makes accurate estimates of the true incidence very difficult to obtain. Diagnosis is based on careful historyand examination.

Automatic detection of anatomical features on 3D ear impressions for canonical representation.

We propose a shape descriptor for 3D ear impressions, derived from a comprehensive set of anatomical features. Motivated by hearing aid (HA) manufacturing, the selection of the anatomical features is carried out according to their uniqueness and importance in HA design. This leads to a canonical ear signature that is highly distinctive and potentially well suited for classification.

Layout consistent segmentation of 3-D meshes via conditional random fields and spatial ordering constraints.

We address the problem of 3-D Mesh segmentation for categories of objects with known part structure. Part labels are derived from a semantic interpretation of non-overlapping subsurfaces. Our approach models the label distribution using a Conditional Random Field (CRF) that imposes constraints on the relative spatial arrangement of neighboring labels, thereby ensuring semantic consistency.

Object recognition through topo-geometric shape models using error-tolerant subgraph isomorphisms.

We propose a method for 3-D shape recognition based on inexact subgraph isomorphisms, by extracting topological and geometric properties of a shape in the form of a shape model, referred to as topo-geometric shape model (TGSM). In a nutshell, TGSM captures topological information through a rigid transformation invariant skeletal graph that is constructed in a Morse theoretic framework with distance function as the Morse function. Geometric information is then retained by analyzing the geometric profile as viewed through the distance function.

STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.

The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis.

Morphological appearance manifolds in computational anatomy: groupwise registration and morphological analysis.

Existing approaches to computational anatomy assume that a perfectly conforming diffeomorphism applied to an anatomy of interest captures its morphological characteristics relative to a template. However, the amount of biological variability in a groupwise analysis renders this task practically impossible, due to the nonexistence of a single template that matches all anatomies in an ensemble, even if such a template is constructed by group averaging procedures. Consequently, anatomical characteristics not captured by the transformation, and which are left out in the residual image, are lost permanently from subsequent analysis, if only properties of the transformation are examined.

Quantification of brain maturation and growth patterns in C57BL/6J mice via computational neuroanatomy of diffusion tensor images.

Diffusion Tensor magnetic resonance imaging and computational neuroanatomy are used to quantify postnatal developmental patterns of C57BL/6J mouse brain. Changes in neuronal organization and myelination occurring as the brain matures into adulthood are examined, and a normative baseline is developed, against which transgenic mice may be compared in genotype-phenotype studies. In early postnatal days, gray matter-based cortical and hippocampal structures exhibit high water diffusion anisotropy, presumably reflecting the radial neuronal organization.

Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images.

Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions.

An anatomical equivalence class based joint transformation-residual descriptor for morphological analysis.

Existing approaches to computational anatomy assume that a perfectly conforming diffeomorphism applied to an anatomy of interest captures its morphological characteristics relative to a template. However, biological variability renders this task extremely difficult, if possible at all in many cases. Consequently, the information not reflected by the transformation, is lost permanently from subsequent analysis.

Flexible skew-symmetric shape model for shape representation, classification, and sampling.

Skewness of shape data often arises in applications (e.g., medical image analysis) and is usually overlooked in statistical shape models.