Accurate segmentation of the left ventricle in computed tomography images for local wall thickness assessment.

In recent years, the fully automatic segmentation of the whole heart from three-dimensional (3D) CT or MR images has become feasible with mean surface accuracies in the order of 1mm. The assessment of local myocardial motion and wall thickness for different heart phases requires highly consistent delineation of the involved surfaces. Papillary muscles and misleading pericardial structures lead to challenges that are not easily resolved. This paper presents a framework to train boundary detection functions to explicitly avoid unwanted structures. A two-pass deformable adaptation process allows to reduce false boundary detections in the first pass while detecting most wanted boundaries in a second pass refinement. Cross-validation tests were performed for 67 cardiac datasets from 33 patients. Mean surface accuracies for the left ventricular endo- and epicardium are 0.76mm and 0.68mm, respectively. The percentage of local outliers with segmentation errors > 2mm is reduced by a factor of 3 as compared to a previously published approach. Wall thickness measurements in full 3D demonstrate that artifacts due to irregular endo- and epicardial contours are drastically reduced.

Patient specific models for planning and guidance of minimally invasive aortic valve implantation.

Recently, new techniques for minimally invasive aortic valve implantation have been developed generating a need for planning tools that assess valve anatomy and guidance tools that support implantation under x-ray guidance. Extracting the aortic valve anatomy from CT images is essential for such tools and we present a model-based method for that purpose. In addition, we present a new method for the detection of the coronary ostia that exploits the model-based segmentation and show, how a number of clinical measurements such as diameters and the distances between aortic valve plane and coronary ostia can be derived that are important for procedure planning. Validation results are based on accurate reference annotations of 20 CT images from different patients and leave-one-out tests. They show that model adaptation can be done with a mean surface-to-surface error of 0.5mm. For coronary ostia detection a success rate of 97.5% is achieved. Depending on the measured quantity, the segmentation translates into a root-mean-square error between 0.4 - 1.2mm when comparing clinical measurements derived from automatic segmentation and from reference annotations.

Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation.

Segmentation of medical images can be achieved with the help of model-based algorithms. Reliable boundary detection is a crucial component to obtain robust and accurate segmentation results and to enable full automation. This is especially important if the anatomy being segmented is too variable to initialize a mean shape model such that all surface regions are close to the desired contours. Several boundary detection algorithms are widely used in the literature. Most use some trained image appearance model to characterize and detect the desired boundaries. Although parameters of the boundary detection can vary over the model surface and are trained on images, their performance (i.e., accuracy and reliability of boundary detection) can only be assessed as an integral part of the entire segmentation algorithm. In particular, assessment of boundary detection cannot be done locally and independently on model parameterization and internal energies controlling geometric model properties. In this paper, we propose a new method for the local assessment of boundary detection called Simulated Search. This method takes any boundary detection function and evaluates its performance for a single model landmark in terms of an estimated geometric boundary detection error. In consequence, boundary detection can be optimized per landmark during model training. We demonstrate the success of the method for cardiac image segmentation. In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme. We also illustrate how the Simulated Search can be used to identify suitable classes of features when addressing a new segmentation task. Finally, we show that the Simulated Search enables multi-modal heart segmentation using a single algorithmic framework. On computed tomography and magnetic resonance images, average segmentation errors (surface-to-surface distances) for the four chambers and the trunks of the large vessels are in the order of 0.8 mm. For 3D rotational X-ray angiography images of the left atrium and pulmonary veins, the average error is 1.3 mm. In all modalities, the locally optimized boundary detection enables fully automatic segmentation.