A regions of confidence based approach to enhance segmentation with shape priors

Abstract

We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.

Automatic detection of left and right ventricles from CTA enables efficient alignment of anatomy with myocardial perfusion data

“…Promising methodologies for the automated extraction of complex anatomical structures rely on the use of model-based segmentation algorithms and on their capability of incorporating prior information about the features of the object to be delineated (17,18). Following this approach, a level set region-based algorithm for the automated detection of LV and RV edges was developed (19,20). The segmentation is constrained by local shape priors retrieved from the statistical analysis of a training datasets and was successfully tested on synthetic images as well as on cardiac acquisitions for myocardium extraction in both 3 and 4D.…”

A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution

“…The proposed method was compared to three types of training-based methods: an active contour method using localized PCA [23], [24], an active shape model (ASM) [4] method, and a standard multi-atlas method with a majority voting scheme [48] for segmenting the pig myocardium using the same dataset. For the latter two methods, the leave-one-out strategy was used due to the small number of images.…”

“…For example, in [22], an active contour model was evolved in the shape space of the left ventricle obtained by applying the PCA to manually segmented images. Local variations may be captured by decomposing images into different regions using prior information for ventricles segmentation [23], [24] or by modeling a shape prior using pixel-wise stochastic level sets to extract the endocardium [25]. A shape constraint was also employed to control the search space of the myocardial contours between two consecutive image slices [26].…”

Automatic Alignment of Myocardial Perfusion Images With Contrast-Enhanced Cardiac Computed Tomography

“…This approach was chosen in order to separate the alignment accuracy from the segmentation accuracy. However, we and others have presented automatic methods for cardiac CTA segmentation [14]–[16]. Commercial techniques are also available and have undergone clinical evaluation [17], [18].…”