Domain Adaptation for Automatic Aorta Segmentation of 4D Flow Magnetic Resonance Imaging Data from Multiple Vendor Scanners

Jordina Aviles, Gonzalo D.Maso Talou, Oscar Camara*, Marcos Mejía Córdova, Xabier Morales Ferez, Daniel Romero, Edward Ferdian, Kathleen Gilbert, Ayah Elsayed, Alistair A. Young, Lydia Dux-Santoy, Aroa Ruiz-Munoz, Gisela Teixido-Tura, Jose Rodriguez-Palomares, Andrea Guala

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

7 Citations (Scopus)

Abstract

The lack of standardized pipelines for image processing has prevented the application of deep learning (DL) techniques for the segmentation of the aorta in phase-contrast enhanced magnetic resonance angiography (PC-MRA). Furthermore, large, well-curated and annotated datasets, which are needed to create DL-based models able to generalize, are rare. We present the adaptation of the popular nnU-net DL framework to automatically segment the aorta in 4D flow MRI-derived angiograms. The resulting segmentations in a large database (> 300 cases) with normal cases and examples of different pathologies of the aorta provided from a single centre were excellent after post-processing (Dice score of 0.944). Subsequently, we explored the generalisation of the trained network in a small dataset of images (around 20 cases) acquired in a different hospital with another scanner. Without domain adaptation, only with a model trained with the large dataset, the obtained results were substantially worst than with adding a few cases of the small dataset (Dice scores of 0.61 vs 0.86, respectively). The obtained results created good quality segmentations of the aorta in 4D flow MRI, which can later be post-processed to assess blood flow patterns, similarly than with manual annotations. However, advanced domain adaptation schemes are very important in 4D flow MRI due to the large differences in image characteristics between different vendor scanners available in multiple centers.

Original languageEnglish
Title of host publicationFunctional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
EditorsDaniel B. Ennis, Luigi E. Perotti, Vicky Y. Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages112-121
Number of pages10
ISBN (Print)9783030787097
DOIs
Publication statusPublished - 2021
Event11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021 - Virtual, Online
Duration: 21 Jun 202125 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12738 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
CityVirtual, Online
Period21/06/202125/06/2021

Keywords

  • 4D flow magnetic resonance imaging
  • Aortic segmentation
  • Deep learning
  • nnU-net

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