Invertible Orientation Scores of 3D Images

The enhancement and detection of elongated structures in noisy image data is relevant for many biomedical applications. To handle complex crossing structures in 2D images, 2D orientation scores were introduced, which already showed their use in a variety of applications. Here we extend this work to 3D orientation scores. First, we construct the orientation score from a given dataset, which is achieved by an invertible coherent state type of transform. For this transformation we introduce 3D versions of the 2D cake-wavelets, which are complex wavelets that can simultaneously detect oriented structures and oriented edges. For efficient implementation of the different steps in the wavelet creation we use a spherical harmonic transform. Finally, we show some first results of practical applications of 3D orientation scores.Comment: ssvm 2015 published version in LNCS contains a mistake (a switch notation spherical angles) that is corrected in this arxiv versio

Subband coding of digital audio signals without loss of quality

A subband coding system for high quality digital audio signals is described. To achieve low bit rates at a high quality level, it exploits the simultaneous masking effect of the human ear. It is shown how this effect can be used in an adaptive bit-allocation scheme. The proposed approach has been applied in two coding systems, a complex system in which signal is split into 26 subbands, each approximately one third of an octave wide, and a simpler 20-band system. Both systems have been designed for coding stereophonic 16-bit compact disk signals with a sampling frequency of 44.1 kHz. With the 26-band system high-quality results can be obtained at bit rates of 220 kb/s. With the 20-band system, similar results can be obtained at bit rates of 360 kb/

Patient-Specific Mappings between Myocardial and Coronary Anatomy

The segmentation of the myocardium based on the 17-segment model as recommended by the American Heart Association is widely used in medical practice. The patient-specific coronary anatomy does not play a role in this model. Due to large variations in coronary anatomy among patients, this can result in an inaccurate mapping between myocardial segments and coronary arteries. We present two approaches to include the patient-specific coronary anatomy in this mapping. The first approach adapts the 17-segment model to fit the patient. The second approach generates a less constrained mapping that does not necessarily conform to this model. Both approaches are based on a Voronoi diagram computation of the primary coronary arteries using geodesic distances along the epicardium in three-dimensional space. We demonstrate both our approaches with several patients and show how our first approach can also be used to fit volume data to the 17-segment model. Our technique gives detailed insight into the coronary anatomy in a single diagram. Based on the feedback provided by clinical experts we conclude that it has the potential to provide a more accurate relation between deficiencies in the myocardium and the supplying coronary arteries

Simulated late gadolinium enhanced cardiac magnetic resonance imaging dataset from mechanical XCAT phantom including a myocardial infarct

The late enhanced magnetic resonance image dataset in this article is simulated using a mechanistic cardiac phantom that includes an myocardial infarct. Settings of the image simulation pipeline are adjusted such that high- and low-resolution images, with and without slice alignment artifacts, are simulated. Our article on the influence of image artifacts on image-based models of the cardiac electrophysiology is based on this data (Kruithof et al., 2021). This dataset provides image-analysis researchers a reference to perform validation of their methods using the included high-resolution ground truth image, a resource that is often unavailable clinically.</p

Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs

We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data for the application of supervised deep-learning (DL) training. The image synthesis consists of label deformation and label-to-image translation tasks. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a label-conditional GAN model. We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease. Furthermore, we propose to model the relationship between 2D slices in the latent space of the VAE prior to reconstruction for generating 3D-consistent subjects from stacking up 2D slice-by-slice generations. We demonstrate that such an approach could provide a solution to diversify and enrich an available database of cardiac MR images and to pave the way for the development of generalizable DL-based image analysis algorithms. We quantitatively evaluate the quality of the synthesized data in an augmentation scenario to achieve generalization and robustness to multi-vendor and multi-disease data for image segmentation. Our code is available at https://github.com/sinaamirrajab/CardiacPathologySynthesis.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/2023:01