TVnet: automated time-resolved tracking of the tricuspid valve plane in MRI long-axis cine images with a dual-stage deep learning pipeline

Tracking the tricuspid valve (TV) in magnetic resonance imaging (MRI) long-axis cine images has the potential to aid in the evaluation of right ventricular dysfunction, which is common in congenital heart disease and pulmonary hypertension. However, this annotation task remains difficult and time-demanding as the TV moves rapidly and is barely distinguishable from the myocardium. This study presents TVnet, a novel dual-stage deep learning pipeline based on ResNet-50 and automated image linear transformation, able to automatically derive tricuspid annular plane systolic excursion. Stage 1 uses a trained network for a coarse detection of the TV points, which are used by stage 2 to reorient the cine into a standardized size, cropping, resolution, and heart orientation and to accurately locate the TV points with another trained network. The model was trained and evaluated on 4170 images from 140 patients with diverse cardiovascular pathologies. A baseline model without standardization achieved a Euclidean distance error of 4.0 ± 3.1 mm and a clinical-metric agreement of ICC = 0.87, whereas a standardized model improved the agreement to 2.4 ± 1.7 mm and an ICC = 0.94, on par with an evaluated inter-observer variability of 2.9 ± 2.9 mm and an ICC = 0.92, respectively. This novel dual-stage deep learning pipeline substantially improved the annotation accuracy compared to a baseline model, paving the way towards reliable right ventricular dysfunction assessment with MRI

MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study

Background Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e’) are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population. Methods The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients. Results MVnet achieved a fast segmentation (<1 s/cine) with excellent ICCs of 0.94 (MAPSE) and 0.93 (LV e’) and a MV plane tracking error of −0.10 ± 0.97 mm. In a similar manner, the inter-observer variability analysis yielded ICCs of 0.95 and 0.89 and a tracking error of −0.15 ± 1.18 mm, respectively. Conclusion A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting

Integrated analysis of anatomical and electrophysiological human intracranial data

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