Unsupervised synthesis of realistic coronary artery X-ray angiogram.

Purpose: Medical image analysis suffers from a sparsity of annotated data necessary in learning-based models. Cardiorespiratory simulators have been developed to counter the lack of data. However, the resulting data often lack realism.

Model-free Cardiorespiratory Motion Prediction from X-ray Angiography Sequence with LSTM Network.

We present a novel model-free approach for cardiorespiratory motion prediction from X-ray angiography time series based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Cardiorespiratory motion prediction is defined as a problem of estimating the future displacement of the coronary vessels in the next image frame in an X-ray angiography sequence. The displacement of the vessels is represented as a sequence of 2D affine transformation matrices allowing 2D X-ray registrations in a sequence.

Epistemic Uncertainty Modeling for Vessel Segmentation.

X-ray angiograms are currently the gold-standard in percutaneous guidance during cardiovascular interventions. However, due to lack of contrast, to overlapping artifacts and to the rapid dilution of the contrast agent, they remain difficult to analyze either by cardiologists, or automatically by computers. Providing, a general yet accurate multi-arteries segmentation method along with the uncertainty linked to those segmentations would not only ease the analysis of medical imaging by cardiologists, but also provide a required pre-processing of the data for tasks ranging from 3D reconstruction to motion tracking of arteries.

CycleGAN for style transfer in X-ray angiography.

Purpose: We aim to perform generation of angiograms for various vascular structures as a mean of data augmentation in learning tasks. The task is to enhance the realism of vessels images generated from an anatomically realistic cardiorespiratory simulator to make them look like real angiographies.

Methods: The enhancement is performed by applying the CycleGAN deep network for transferring the style of real angiograms acquired during percutaneous interventions into a data set composed of realistically simulated arteries.