TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline

Ricardo A. Gonzales, Jérôme Lamy, Felicia Seemann, Einar Heiberg, John A. Onofrey, Dana C. Peters

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingForskningPeer review

Sammanfattning

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.

Originalspråkengelska
Titel på gästpublikationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
RedaktörerMarleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
FörlagSpringer Science and Business Media B.V.
Sidor567-576
Antal sidor10
ISBN (tryckt)9783030872304
DOI
StatusPublished - 2021
Evenemang24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Varaktighet: 2021 sep 272021 okt 1

Publikationsserier

NamnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volym12906 LNCS
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349

Konferens

Konferens24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
OrtVirtual, Online
Period2021/09/272021/10/01

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© 2021, Springer Nature Switzerland AG.

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