Eric Z. Chen

Lin Zhao, Xin Yu, Yikang Liu et al.

Accurate correspondence matching in coronary angiography images is crucial for reconstructing 3D coronary artery structures, which is essential for precise diagnosis and treatment planning of coronary artery disease (CAD). Traditional matching methods for natural images often fail to generalize to X-ray images due to inherent differences such as lack of texture, lower contrast, and overlapping structures, compounded by insufficient training data. To address these challenges, we propose a novel pipeline that generates realistic paired coronary angiography images using a diffusion model conditioned on 2D projections of 3D reconstructed meshes from Coronary Computed Tomography Angiography (CCTA), providing high-quality synthetic data for training. Additionally, we employ large-scale image foundation models to guide feature aggregation, enhancing correspondence matching accuracy by focusing on semantically relevant regions and keypoints. Our approach demonstrates superior matching performance on synthetic datasets and effectively generalizes to real-world datasets, offering a practical solution for this task. Furthermore, our work investigates the efficacy of different foundation models in correspondence matching, providing novel insights into leveraging advanced image foundation models for medical imaging applications.

Pingjun Chen, Xiao Chen, Eric Z. Chen et al.

Cardiac motion estimation is critical to the assessment of cardiac function. Myocardium feature tracking (FT) can directly estimate cardiac motion from cine MRI, which requires no special scanning procedure. However, current deep learning-based FT methods may result in unrealistic myocardium shapes since the learning is solely guided by image intensities without considering anatomy. On the other hand, motion estimation through learning is challenging because ground-truth motion fields are almost impossible to obtain. In this study, we propose a novel Anatomy-Aware Tracker (AATracker) for cardiac motion estimation that preserves anatomy by weak supervision. A convolutional variational autoencoder (VAE) is trained to encapsulate realistic myocardium shapes. A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE. We evaluate the proposed method on long-axis cardiac cine MRI, which has more complex myocardium appearances and motions than short-axis. Compared with other methods, AATracker significantly improves the tracking performance and provides visually more realistic tracking results, demonstrating the effectiveness of the proposed weakly-supervision scheme in cardiac motion estimation.

Xiaoxiao Li, Junyan Wu, Eric Z. Chen et al.

Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists. Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis. Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making. However, much research focuses on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesions. Secondly, we utilize a prediction difference analysis method that examines each patch on the image through patch-wised corrupting to detect the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings can be significant and useful to guide clinical diagnosis.