Simultaneous segmentation of the left and right heart ventricles in 3D cine MR images of small animals

New high resolution image techniques allow to capture the anatomy and movement of the heart of small animals. The availability of these in vivo images can be very useful for medical research, however the amount of generated data for large animal studies makes manual analysis a very tedious task. To cope with the problem of automatic analysis of these images, we propose the use of the Deformable Elastic Template method to perform automatic segmentation of the ventricles. To adapt the method to the specificities of high-resolution MRI, several improvements are presented, including an image-context dependent scheme for more robust segmentation. Qualitative results show that our method is able to correctly retrieve the heart’s contours in 3D. 1

A dynamic 3-D cardiac surface model from MR images

Cardiac 3D + time segmentation and motion estimation are recognized as difficult prerequisite tasks for any quan-titative analysis of cardiac images. Some recent algorithms aim to consider a temporal constraint to increase the ac-curacy of results. To improve the temporal consistency, prior knowledge about cardiac dynamics can be used. In this paper, we propose to build a new Statistical Dynamic Model (SDM) of the heart by learning through a popula-tion of healthy individuals. This SDM is composed by a set of semi-landmarks which describe the heart surfaces. For each of them, a mean trajectory and variability around it are derived. The SDM provides a reasonable constraint for a temporally regularized segmentation and motion track-ing algorithm. 1

Mammographic density. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations

Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment

Purpose: The purpose of the study was to evaluate the usefulness of power law spectral analysis on mammographic parenchymal patterns in breast cancer risk assessment. Materials and Methods: Mammograms from 172 subjects (30 women with the BRCA1/BRCA2 gene mutation and 142 low-risk women) were retrospectively collected and digitized. Because age is a very important risk factor, 60 low-risk women were randomly selected from the 142 low-risk subjects and were age matched to the 30 gene mutation carriers. Regions of interest were manually selected from the central breast region behind the nipple of these digitized mammograms and subsequently used in power spectral analysis. The power law spectrum of the form \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}P\left( f \right) = {B \mathord{\left/ {\vphantom {B {f^\beta }}} \right. \kern-\nulldelimiterspace} {f^\beta }}\end{document} was evaluated for the mammographic patterns. The performance of exponent β as a decision variable for differentiating between gene mutation carriers and low-risk women was assessed using receiver operating characteristic analysis for both the entire database and the age-matched subset. Results: Power spectral analysis of mammograms demonstrated a statistically significant difference between the 30 BRCA1/BRCA2 gene mutation carriers and the 142 low risk women with an average β values of 2.92 (±0.28) and 2.47(±0.20), respectively. An Az value of 0.90 was achieved in distinguishing between gene mutation carriers and low-risk women in the entire database, with an Az value of 0.89 being achieved on the age-matched subset. Conclusions: The BRCA1/BRCA2 gene mutation carriers and low-risk women have different mammographic parenchymal patterns. It is expected that women identified as high risk by computerized feature analyses might potentially be more aggressively screened for breast cancer