XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms

Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureHoofdstukAcademicpeer review

1 Citaat (Scopus)
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Samenvatting

Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation technique: 4-class and 8-class XCAT-GANs. The 4-class technique relies on only the annotations of the heart; while the 8-class technique employs a predicted multi-tissue label map of the heart-surrounding organs and provides better guidance for our conditional image synthesis. For both techniques, we train our conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subjects of synthetic CMR images at the end-diastolic and end-systolic phases, we evaluate the usefulness of such data in the downstream cardiac cavity segmentation task under different augmentation strategies. Results demonstrate that even with only 20% of real images (40 volumes) seen during training, segmentation performance is retained with the addition of synthetic CMR images. Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
Originele taal-2Engels
TitelMedical Image Computing and Computer Assisted Intervention – MICCAI 2020
Subtitel23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
RedacteurenAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Plaats van productieCham
UitgeverijSpringer
Pagina's128-137
Aantal pagina's10
ISBN van elektronische versie978-3-030-59719-1
ISBN van geprinte versie978-3-030-59718-4
DOI's
StatusGepubliceerd - 27 jul 2020

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12264 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Bibliografische nota

Accepted for MICCAI 2020

Trefwoorden

  • eess.IV
  • cs.CV
  • cs.LG

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