Tommaso Mansi

TL;DR: This paper demonstrates, on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-art registration methods by a large margin in terms of both accuracy and robustness.

TL;DR: A novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium; the results show that the proposed method, trained on synthetically generated data, may generalize to real data.

TL;DR: In this paper, a graph convolutional neural network is used to estimate biventricular activation times from sparse measurements, which is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model.

TL;DR: In this article, a method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject is described. But the method is not suitable for the classification of real images.

TL;DR: This paper proposes a semi-supervised spatial-temporal modeling framework for real-time anatomical landmark tracking in 3D transesophageal echocardiography (TEE) images, and demonstrates that by combining a discriminative feature extractor with a generative tracking model, it could achieve superior performance using a small number of annotated data compared to state-of-the-art methods.