Marie-Pierre Jolly

Interactive Organ Segmentation Using Graph Cuts

An N-dimensional image is divided into “object” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds providing necessary clues about the image content. Our objective is to find the cheapest way to cut the edges in the graph so that the object seeds are completely separated from the background seeds. If the edge cost is a decreasing function of the local intensity gradient then the minimum cost cut should produce an object/background segmentation with compact boundaries along the high intensity gradient values in the image. An efficient, globally optimal solution is possible via standard min-cut/max-flow algorithms for graphs with two terminals. We applied this technique to interactively segment organs in various 2D and 3D medical images.

Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images

This paper describes a segmentation technique to automatically extract the myocardium in 4D cardiac MR and CT datasets. The segmentation algorithm is a two step process. The global localization step roughly localizes the left ventricle using techniques such as maximum discrimination, thresholding and connected component analysis. The local deformations step combines EM-based region segmentation and Dijkstra active contours using graph cuts, spline fitting, or point pattern matching. The technique has been tested on a large number of patients and both quantitative and qualitative results are presented.

Motion correction for myocardial T1 mapping using image registration with synthetic image estimation

Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and nonischemic cardiomyopathies. Image acquisition using the modified Look‐Locker inversion recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this article, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust nonrigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map. Magn Reson Med, 2011.

Automatic heart isolation for CT coronary visualization using graph-cuts

We describe a means to automatically and efficiently isolate the outer surface of the entire heart in computer tomography (CT) cardiac scans. Isolating the entire heart allows the coronary vessels on the surface of the heart to be easily visualized despite the proximity of surrounding organs such as the ribs and pulmonary blood vessels. Numerous techniques have been described for segmenting the left ventricle of the heart in images from various types of medical scanners but rarely has the entire heart been segmented. We make use of graph-cuts to do the segmentation and introduce a novel means of initiating and constraining the graph-cut technique for heart isolation. The technique has been tested on 70 patient data sets. Results are compares with hand labeled results

3D general lesion segmentation in CT

This paper describes a general purpose algorithm to segment any kind of lesions in CT images. The algorithm expects a click or a stroke inside the lesion from the user and learns gray level properties on the fly. It then uses the random walker algorithm and combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion. Quantitative evaluation on 293 lesions demonstrates that the method is ready for clinical use.

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73±11.24years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

Phase-Sensitive Inversion Recovery for Myocardial T1 Mapping with Motion Correction and Parametric Fitting

The assessment of myocardial fibrosis and extracellular volume requires accurate estimation of myocardial T1s. While image acquisition using the modified Look‐Locker inversion recovery technique is clinically feasible for myocardial T1 mapping, respiratory motion can limit its applicability. Moreover, the conventional T1 fitting approach using the magnitude inversion recovery images can lead to less stable T1 estimates and increased computational cost. In this article, we propose a novel T1 mapping scheme that is based on phase‐sensitive image reconstruction and the restoration of polarity of the MR signal after inversion. The motion correction is achieved by registering the reconstructed images after background phase removal. The restored signal polarity of the inversion recovery signal helps the T1 fitting resulting in improved quality of the T1 map and reducing the computational cost. Quantitative validation on a data cohort of 45 patients proves the robustness of the proposed method against varying image contrast. Compared to the magnitude T1 fitting, the proposed phase‐sensitive method leads to less fluctuation in T1 estimates. Magn Reson Med, 2013.

Integrated registration of dynamic renal perfusion MR images

This paper presents an integrated image registration algorithm to correct the motion induced by patient breathing for dynamic renal perfusion MR images. Registration of kidneys through the MR image sequence is a challenging task due to rapidly changing image contrast over the course of contrast enhancement. Our algorithm achieves temporal image registration in a multi-step fashion. We first roughly register the images by detecting large-scale motion, and then refine the registration results by integrating region information and local gradient information with auxiliary image segmentation results. We have tested the proposed algorithm on several real patients and obtained excellent registration results.

Segmentation of the left ventricle in cardiac MR images

This paper describes a segmentation technique to automatically extract the myocardium in 4D cardiac MR images for quantitative cardiac analysis and the diagnosis of patients. Three different modules are presented. The automatic localization algorithm is able to approximately locate the left ventricle in an image using a maximum discrimination technique. Then, the local deformation algorithm can deform active contours so that they align to the edges in the image to produce the desired outlining of the myocardium. Finally, the global localization algorithm is able to propagate segmented contours from one image in the data set to all the others. We have experimented with the proposed method on a large number of patients and present some examples to show the strengths and pitfalls of our algorithm.

Weights and Topology: A Study of the Effects of Graph Construction on 3D Image Segmentation

Graph-based algorithms have become increasingly popular for medical image segmentation. The fundamental process for each of these algorithms is to use the image content to generate a set of weights for the graph and then set conditions for an optimal partition of the graph with respect to these weights. To date, the heuristics used for generating the weighted graphs from image intensities have largely been ignored, while the primary focus of attention has been on the details of providing the partitioning conditions. In this paper we empirically study the effects of graph connectivity and weighting function on the quality of the segmentation results. To control for algorithm-specific effects, we employ both the Graph Cuts and Random Walker algorithms in our experiments.