Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images

Daniel H. Pak, Minliang Liu, Shawn S. Ahn, Andrés Caballero, John A. Onofrey, Liang Liang, Wei Sun, James S. Duncan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Finite Element Analysis (FEA) is useful for simulating Transcather Aortic Valve Replacement (TAVR), but has a significant bottleneck at input mesh generation. Existing automated methods for imaging-based valve modeling often make heavy assumptions about imaging characteristics and/or output mesh topology, limiting their adaptability. In this work, we propose a deep learning-based deformation strategy for producing aortic valve FE meshes from noisy 3D CT scans of TAVR patients. In particular, we propose a novel image analysis problem formulation that allows for training of mesh prediction models using segmentation labels (i.e. weak supervision), and identify a unique set of losses that improve model performance within this framework. Our method can handle images with large amounts of calcification and low contrast, and is compatible with predicting both surface and volumetric meshes. The predicted meshes have good surface and correspondence accuracy, and produce reasonable FEA results.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings
EditorsAasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages637-648
Number of pages12
ISBN (Print)9783030781903
DOIs
StatePublished - 2021
Event27th International Conference on Information Processing in Medical Imaging, IPMI 2021 - Virtual, Online
Duration: Jun 28 2021Jun 30 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12729 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Information Processing in Medical Imaging, IPMI 2021
CityVirtual, Online
Period6/28/216/30/21

Keywords

  • Aortic valve modeling
  • Shape deformation
  • Weakly supervised deep learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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