This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth. DeNVeR utilizes optical flow and layer separation techniques, enhancing segmentation accuracy and adaptability through test-time training. Key contributions include a novel layer separation bootstrapping technique, a parallel vessel motion loss, and the integration of Eulerian motion fields for modeling complex vessel dynamics. A significant component of this research is the introduction of the XACV dataset, the first X-ray angiography coronary video dataset with high-quality, manually labeled segmentation ground truth. Extensive evaluations on both XACV and CADICA datasets demonstrate that DeNVeR outperforms current state-of-the-art methods in vessel segmentation accuracy and generalization capability while maintaining temporal coherency. Please see our project page at kirito878.github.io/DeNVeR.

Although sequential Coronary Angiograms (CAG) are often used in cardiac catheterizations, most vessel segmentation techniques are developed just for single images, owing to the expense of collecting and labeling huge amounts of sequential X-Ray images. The CAG segmentation using single images could cause the overlook of critical temporal information among images. Existing video segmentation methods, while they achieve high accuracy for daily life videos, cannot be used directly for sequential CAG, because they are not trained to capture the special structure of coronary articles. In this work, we investigate data manipulation strategies for training sequential CAG segmentation without labeled video data. We leveraged a general-purpose video segmentation model, XMem, for sequential CAG segmentation. We have proposed three data manipulation strategies for model training, including (1) sequence reversal for CAG data; (2) data hybridization for model training; (3) pseudo labels for sequential CAG data. The experimental results show that with good training strategies, one can use general-purpose video segmentation networks for sequential CAG data without explicitly labeled data. The best result of our trained network can achieve an average F1 score of 85.04% and an average region similarity of 73.97%, both of which are higher than the state-of-the-art results.

To better identify the vessels, image segmentation techniques are often applied to coronary angiography (CAG) which reveals the functions and structures of heart's arteries using X-Ray images. Although deep learning based segmentation methods have shown their superiority in accuracy, they are often too complex for medical edge computing, a way to provides prompt diagnoses with minimum hardware cost. In this study, we investigate the method for CAG segmentation on mobile edge devices and propose a novel method, called Enhanced Frangi Filter for Coronary Angiography (EFFCA). Frangi filter is a classical method for vessel segmentation, but suffers from the problems of long processing time for multi-scale search and the vessel breakage problem. EFFCA utilizes a lightweight neural network to recognize the vessel patterns to decide the most suitable scales. It also employs the statistical and connectivity information of vessels to fix the vessel breakage from the segmented results. We have implemented EFFCA on mobile devices to demonstrate its usability. Experimental results show that EFFCA achieves a segmentation accuracy of 95.6% and a specificity of 96.2%, similar to the results of state-of-the-art models. Additionally, EFFCA offers the advantages of a much smaller code size, an efficient training process, and faster inference times on mobile edge devices.

The proliferation of wearable devices has allowed the collection of electrocardiogram (ECG) recordings daily to monitor heart rhythm and rate. For example, 24-hour Holter monitors, cardiac patches, and smartwatches are widely used for ECG gathering and application. An automatic atrial fibrillation (AF) detector is required for timely ECG interpretation. Deep learning models can accurately identify AFs if large amounts of annotated data are available for model training. However, it is impractical to request sufficient labels for ECG recordings for an individual patient to train a personalized model. We propose a Siamese-network-based approach for transfer learning to address this issue. A pre-trained Siamese convolutional neural network is created by comparing two labeled ECG segments from the same patient. We sampled 30-second ECG segments with a 50% overlapping window from the ECG recordings of patients in the MIT-BIH Atrial Fibrillation Database. Subsequently, we independently detected the occurrence of AF in each patient in the Long-Term AF Database. By fine-tuning the model with the 1, 3, 5, 7, 9, or 11 ECG segments ranging from 30 to 180 s, our method achieved macro-F1 scores of 96.84%, 96.91%, 96.97%, 97.02%, 97.05%, and 97.07%, respectively.