Morphometric tools for landmark data: geometry and biology

Statistical shape models for 3D medical image segmentation: a review.

We review methods designed to compute correspondences between geometric shapes represented by triangle meshes, contours or point sets. This survey is motivated in part by recent developments in space–time registration, where one seeks a correspondence between non-rigid and time-varying surfaces, and semantic shape analysis, which underlines a recent trend to incorporate shape understanding into the analysis pipeline. Establishing a meaningful correspondence between shapes is often difficult because it generally requires an understanding of the structure of the shapes at both the local and global levels, and sometimes the functionality of the shape parts as well. Despite its inherent complexity, shape correspondence is a recurrent problem and an essential component of numerous geometry processing applications. In this survey, we discuss the different forms of the correspondence problem and review the main solution methods, aided by several classification criteria arising from the problem definition. The main categories of classification are defined in terms of the input and output representation, objective function and solution approach. We conclude the survey by discussing open problems and future perspectives.

/pdf/a-survey-on-shape-correspondence-5gnpsguhxd.pdf

A Survey on Shape Correspondence

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems Its main limitations is the necessity of spherical topologyThe input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM) After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance informationThe correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives

http://pnl.bwh.harvard.edu/pub/pdfs/StynerMICCAI2006.pdf

Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM

In this article, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely, capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research, and highlighting the broad range of current and future applications.

/pdf/3d-morphable-face-models-past-present-and-future-33bjgatyz8.pdf

3D Morphable Face Models—Past, Present, and Future

The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of four different correspondence establishing methods. The goal in all of the presented studies is a model-based application. We have analyzed both the direct correspondence via manually selected landmarks as well as the properties of the model implied by the correspondences, in regard to compactness, generalization and specificity. The studied methods include a manually initialized subdivision surface (MSS) method and three automatic methods that optimize the object parameterization: SPHARM, MDL and the covariance determinant (DetCov) method. In all studies, DetCov and MDL showed very similar results. The model properties of DetCov and MDL were better than SPHARM and MSS. The results suggest that for modeling purposes the best of the studied correspondence method are MDL and DetCov.

/pdf/evaluation-of-3d-correspondence-methods-for-model-building-4rr97ypcmp.pdf

Evaluation of 3D correspondence methods for model building.

Acute aortic dissection is a life-threatening condition and must be diagnosed and treated promptly. For treatment planning the reliable identification of the true and false lumen is crucial. However, a fully automatic Computer Aided Diagnosing system capable to display the different lumens in an easily comprehensible and timely manner is still not available.

Automatic Segmentation of the Vessel Lumen from 3D CTA Images of Aortic Dissection.

Temporal prediction of respiratory motion is required due to the latencies in image-guided therapy systems In this study we propose to combine the outcome of four temporal prediction methods, which have different strength and weaknesses, by taking their median Based on 25 motion traces from ultrasound liver tracking, this decision fusion provided statistically significantly better results than the individual outcomes for latencies from 150 to 1000ms On average, RMS errors reduced by at least 50% in comparison to assuming no motion for all latencies Furthermore it was shown that time-intensive optimization of the methods parameters to individual cases was not required, as performance from using the median parameters from the population was not significantly worse when using decision fusion

Decision fusion for temporal prediction of respiratory liver motion

A drawback of the commonly used single source computed tomography systems (CT) is that different materials might show very similar attenuation at any selected radiation energy. However, the assessment of atherosclerosis requires good differentiation between vessel lumen, calcium, adipose, and surrounding tissue. Dual energy CT (DECT) simultaneously measures attenuations at two energies and therefore can improve the differentiation to some extent. A tissue cancelation and enhancement algorithm for dual energy data was already proposed in 1981 and evaluated on experimental settings with a stationary X-ray source. For this study, we adapted this algorithm for DECT and propose its usage as a pre-processing step for the assessment of atherosclerosis. On clinical DECT patient data and with fixed parameters we could show a simultaneous contrast enhancement between 8% and 67% among all targeted tissues.

/pdf/contrast-enhancement-with-dual-energy-ct-for-the-assessment-35dr102lvn.pdf

Contrast Enhancement with Dual Energy CT for the Assessment of Atherosclerosis

A simulation environment for validation and comparison of fMRI evaluation procedures

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