C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for Medical Image Segmentation

Maria G. Baldeon Calisto, Susana K. Lai-Yuen

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

1 Scopus citations


Deep learning models have obtained state-of-the-art results for medical image analysis. However, CNNs require a massive amount of labelled data to achieve a high performance. Moreover, many supervised learning approaches assume that the training/source dataset and test/target dataset follow the same probability distribution. Nevertheless, this assumption is hardly satisfied in real-world data and when the models are tested on an unseen domain there is a significant performance degradation. In this work, we present an unsupervised Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical image segmentation. C-MADA implements an image-level and feature-level adaptation method in a two-step sequential manner. First, images from the source domain are translated to the target domain through an unpaired image-to-image adversarial translation with cycle-consistency loss. Then, a U-Net network is trained with the mapped source domain images and target domain images in an adversarial manner to learn domain-invariant feature representations and produce segmentations for the target domain. Furthermore, to improve the network’s segmentation performance, information about the shape, texture, and contour of the predicted segmentation is included during the adversarial training. C-MADA is tested on the task of brain MRI segmentation from the crossMoDa Grand Challenge and is ranked within the top 15 submissions of the challenge.

Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew
ISBN (Electronic)9781510649392
StatePublished - 2022
EventMedical Imaging 2022: Image Processing - Virtual, Online
Duration: 21 Mar 202127 Mar 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Image Processing
CityVirtual, Online


  • Domain Adaptation
  • Generative Adversarial Networks
  • Image Segmentation
  • Medical Image Analysis
  • Unsupervised Learning


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