Deep Reinforcement Learning for Localization of the Aortic Annulus in Patients with Aortic Dissection

Marina Codari*, Antonio Pepe, Gabriel Mistelbauer, D. Mastrodicasa, S. Walters, M. J. Willemink, D. Fleischmann

*Corresponding author for this work

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

Abstract

Accurate localization of the aortic annulus is key to several imaging tasks, like cross-sectional aortic valve plane estimation, aortic root segmentation, and annulus diameter measurements. In this project, we propose an end-to-end trainable deep reinforcement learning (DRL) algorithm aimed at identification of the aortic annulus in patients with aortic dissection. We trained 5 different agents on a dataset of 75 CT scans from 66 patients following a sequential model-upgrading strategy. We evaluated the effect of performing different image preprocessing steps, adding batch normalization and regularization layers, and changing terminal state definition. At each step of this sequential process, the model performance has been evaluated on a validation sample composed of 24 CTA scans from 24 independent patients. Localization accuracy was defined as the Euclidean distance between estimated and target aortic annulus locations. Best model results show a median localization error equal to 2.98 mm with an interquartile range equal to [2.25, 3.81] mm, and a failure rate (i.e., percentage of samples with localization error in validation data. We proved the feasibility of DRL application for aortic annulus localization in CTA images of patients with aortic dissection, which are characterized by a large variability in aortic morphology and image quality. Nevertheless, further improvements are needed to reach expert-human level performance.

Original languageEnglish
Title of host publicationThoracic Image Analysis - Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
Subtitle of host publicationSecond International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
EditorsJens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Sarah Gerard, Bianca Lassen-Schmidt, Colin Jacobs, Reinhard Beichel, Kensaku Mori
Place of PublicationCham
PublisherSpringer
Pages94-105
Number of pages12
ISBN (Electronic)978-3-030-62469-9
ISBN (Print)978-3-030-62468-2
DOIs
Publication statusPublished - 1 Jan 2020
Event2nd International Workshop on Thoracic Image Analysis - Virtuell, Peru
Duration: 8 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12502 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Thoracic Image Analysis
Abbreviated titleTIA 2020
CountryPeru
CityVirtuell
Period8/10/208/10/20

Keywords

  • Aortic annulus
  • Deep reinforcement learning
  • Landmark localization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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