We develop and test a new method for automatic determination of vessel wall diameters from image stacks obtained using two-photon laser scanning microscopy (TPLSM) on viable arteries in perfusion flow chambers. To this extent, a new method is proposed for estimating the parameters of a circle describing the inner diameter of the blood vessels. The new method is based on the Hough transform and the observation that three points that are not colinear uniquely define a circle. By only storing the estimated center location, the computational and memory costs of the Hough transform can be greatly reduced. We test the algorithm on 20 images and compare the result with a ground-truth established by human volunteers and a standard least-squares technique. With errors of 3 to 5%, the algorithm enables accurate estimation of the vessel diameters from image stacks containing only small parts of the vessel cross section. Combined with TPLSM imaging of anatomical vessel wall properties, potentially, the algorithm enables the correlation of structural and functional properties of large intact arteries simultaneously, without requirements for additional experiments.
Manual quantitative analysis of cardiac left ventricular function using multi-slice CT is labor intensive because of the large datasets. We present an automatic, robust and intrinsically three-dimensional segmentation method for cardiac CT images, based on 3D Active Shape Models (ASMs). ASMs describe shape and shape variations over a population as a mean shape and a number of eigenvariations, which can be extracted by e.g. Principal Component Analysis (PCA). During the iterative ASM matching process, the shape deformation is restricted within statistically plausible constraints (±3σ). Our approach has two novel aspects: the 3D-ASM application to volume data of arbitrary planar orientation, and the application to image data from another modality than which was used to train the model, without the necessity of retraining it. The 3D-ASM was trained on MR data and quantitatively evaluated on 17 multi-slice cardiac CT data sets, with respect to calculated LV volume (blood pool plus myocardium) and endocardial volume. In all cases, model matching was convergent and final results showed a good model performance. Bland-Altman analysis however, showed that bloodpool volume was slightly underestimated and LV volume was slightly overestimated by the model. Nevertheless, these errors remain within clinically acceptable margins. Based on this evaluation, we conclude that our 3D-ASM combines robustness with clinically acceptable accuracy. Without retraining for cardiac CT, we could adapt a model trained on cardiac MR data sets for application in cardiac CT volumes, demonstrating the flexibility and feasibility of our matching approach. Causes for the systematic errors are edge detection, model constraints, or image data reconstruction. For all these categories, solutions are discussed.