A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection

Left Ventricle Detection from Cardiac Magnetic Resonance Relaxometry Images Using Visual Transformer

Left Ventricle (LV) detection from Cardiac Magnetic Resonance (CMR) imaging is a fundamental step, preliminary to myocardium segmentation and characterization. This paper focuses on the application of a Visual Transformer (ViT), a novel neural network architecture, to automatically detect LV from CMR relaxometry sequences. We implemented an object detector based on the ViT model to identify LV from CMR multi-echo T2* sequences. We evaluated performances differentiated by slice location according to the American Heart Association model using 5-fold cross-validation and on an independent dataset of CMR T2*, T2, and T1 acquisitions. To the best of our knowledge, this is the first attempt to localize LV from relaxometry sequences and the first application of ViT for LV detection. We collected an Intersection over Union (IoU) index of 0.68 and a Correct Identification Rate (CIR) of blood pool centroid of 0.99, comparable with other state-of-the-art methods. IoU and CIR values were significantly lower in apical slices. No significant differences in performances were assessed on independent T2* dataset (IoU = 0.68, p = 0.405; CIR = 0.94, p = 0.066). Performances were significantly worse on the T2 and T1 independent datasets (T2: IoU = 0.62, CIR = 0.95; T1: IoU = 0.67, CIR = 0.98), but still encouraging considering the different types of acquisition. This study confirms the feasibility of the application of ViT architectures in LV detection and defines a benchmark for relaxometry imaging.

Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning

Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV). In this paper, U-Net architecture is proposed for the automatic segmentation of THV on angiographies, studying the role of its hyperparameters in the quality of the segmentations. Several experiments have been conducted, testing the methodology using multiple configurations and evaluating the results on different types of frames captured during the procedure. The evaluation has been performed in terms of conventional classification metrics, complemented with two new metrics, specifically defined for this problem. Those new metrics provide a more appropriate assessment of the quality of the results, given the class imbalance in the dataset. From an analysis of the evaluation results, it can be concluded that the method provides appropriate segmentation results for this dataset.