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

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

We propose a method for synthesizing cardiac MR images with plausible heart shapes and realistic appearances for the purpose of generating labeled data for deep-learning (DL) training. It breaks down the image synthesis into 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 conditional GAN model. We devise an approach for label manipulation in the latent space of the trained VAE model, namely 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 via estimating the correlation coefficient matrix between the latent vectors and utilizing it to correlate elements of randomly drawn samples before decoding to image space. This simple yet effective approach results in generating 3D consistent subjects from 2D slice-by-slice generations. Such an approach could provide a solution to diversify and enrich the available database of cardiac MR images and to pave the way for the development of generalizable DL-based image analysis algorithms. The code will be available at https://github.com/sinaamirrajab/CardiacPathologySynthesis

Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation

Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However, obtaining all required sequences and expertly labeled data for training is challenging and can result in decreased quality of segmentation models developed through automated algorithms. In this work, we examine the possibility of employing a conditional generative adversarial network (GAN) approach for synthesizing multi-modal images to train deep learning-based neural networks aimed at high-grade glioma (HGG) segmentation. The proposed GAN is conditioned on auxiliary brain tissue and tumor segmentation masks, allowing us to attain better accuracy and control of tissue appearance during synthesis. To reduce the domain shift between synthetic and real MR images, we additionally adapt the low-frequency Fourier space components of synthetic data, reflecting the style of the image, to those of real data. We demonstrate the impact of Fourier domain adaptation (FDA) on the training of 3D segmentation networks and attain significant improvements in both the segmentation performance and prediction confidence. Similar outcomes are seen when such data is used as a training augmentation alongside the available real images. In fact, experiments on the BraTS2020 dataset reveal that models trained solely with synthetic data exhibit an improvement of up to 4% in Dice score when using FDA, while training with both real and FDA-processed synthetic data through augmentation results in an improvement of up to 5% in Dice compared to using real data alone. This study highlights the importance of considering image frequency in generative approaches for medical image synthesis and offers a promising approach to address data scarcity in medical imaging segmentation.</p

Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing

Fast detection of hydrogen gas leakage or its release in different environments, especially in large electric vehicle batteries, is a major challenge for sensing applications. In this study, the morphological, structural, chemical, optical, and electronic characterizations of ZnO:Eu nanowire arrays are reported and discussed in detail. In particular, the influence of different Eu concentrations during electrochemical deposition was investigated together with the sensing properties and mechanism. Surprisingly, by using only 10 μM Eu ions during deposition, the value of the gas response increased by a factor of nearly 130 compared to an undoped ZnO nanowire and we found an H2gas response of ∼7860 for a single ZnO:Eu nanowire device. Further, the synthesized nanowire sensors were tested with ultraviolet (UV) light and a range of test gases, showing a UV responsiveness of ∼12.8 and a good selectivity to 100 ppm H2gas. A dual-mode nanosensor is shown to detect UV/H2gas simultaneously for selective detection of H2during UV irradiation and its effect on the sensing mechanism. The nanowire sensing approach here demonstrates the feasibility of using such small devices to detect hydrogen leaks in harsh, small-scale environments, for example, stacked battery packs in mobile applications. In addition, the results obtained are supported through density functional theory-based simulations, which highlight the importance of rare earth nanoparticles on the oxide surface for improved sensitivity and selectivity of gas sensors, even at room temperature, thereby allowing, for instance, lower power consumption and denser deployment