Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences

Left ventricle segmentation combining deep learning and deformable models with anatomical constraints

Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required from the expert, many automatic segmentation methods have been proposed, in which deep learning networks have obtained remarkable performance. However, one of the main limitations of these approaches is the production of segmentations that contain anatomical errors. To avoid this limitation, we propose a new fully-automatic left ventricle segmentation method combining deep learning and deformable models. We propose a new level set energy formulation that includes exam-specific information estimated from the deep learning segmentation and shape constraints. The method is part of a pipeline containing pre-processing steps and a failure correction post-processing step. Experiments were conducted with the Sunnybrook and ACDC public datasets, and a private dataset. Results suggest that the method is competitive, that it can produce anatomically consistent segmentations, has good generalization ability, and is often able to estimate biomarkers close to the expert.

Validation of a diffeomorphic registration algorithm using true deformation computed from thin plate spline interpolation

Despite the inter and intraobserver variabilities, manual contours are commonly used as surrogates for ground truth in the validation process for nonrigid medical image registration. In contrast, this study proposes the use of thin plate spline interpolation to create a true deformation field. A diffeomorphic registration method was compared to the true deformation field along with three other algorithms and was evaluated on simulated cardiac motion deformation over 10 subjects from the Automated Cardiac Diagnosis Challenge (ACDC) dataset. Two sequential registration approaches were undertaken: with respect to the first frame, and with respect to the previous frame. The Dice score was calculated between the simulated and warped contours for the two approaches: diffeomorphic registration method =0.991 and 0.997, RealTITracker (L₂L₂ method) = 0.971 and 0.977, RealTITracker (L₂L₁ method) = 0.975 and 0.978, and Elastix = 0.976 and 0.994. The results demonstrate the robust performance of the diffeomorphic registration method.Clinical relevance This establishes a validation of a registration method that can be used for segmentation of chambers of the heart.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.