An Extensive Pixel-Level Augmentation Framework for Unsupervised Cross-Modality Domain Adaptation

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


Convolutional neural networks (CNNs) have achieved great success in automating the segmentation of medical images. Nevertheless, when a trained CNN is tested on a new domain there is a performance degradation due to the distribution shift. In this work, we present an unsupervised Extensive Pixel-level Augmentation framework (EPA) for cross-modality domain adaptation. EPA implements a two-phase image- and feature-level adaptation method. In the first phase, the source domain images are mapped to target domain in pixel space using the CycleGAN, StAC-DA, and CUT translation models. This creates an augmented translated dataset 3 times bigger than the original. In phase 2, a deeply supervised U-Net network is trained to segment the target images using a semi-supervised adversarial learning approach. In particular, a set of discriminator networks are trained to distinguish between the target and source domain segmentations, while the U-Net aims to fool them. EPA is tested on the task of brain structure segmentation from the Crossmoda 2022 Grand Challenge, being ranked within the top 12 submissions of the testing phase. Moreover, we demonstrate that augmenting the size of the mapped dataset through distinct translation methods is crucial for increasing the segmentation accuracy of the model.

Idioma originalInglés
Título de la publicación alojadaMedical Imaging 2023
Subtítulo de la publicación alojadaImage Processing
EditoresOlivier Colliot, Ivana Isgum
ISBN (versión digital)9781510660335
EstadoPublicada - 3 abr. 2023
EventoMedical Imaging 2023: Image Processing - San Diego, Estados Unidos
Duración: 19 feb. 202323 feb. 2023

Serie de la publicación

NombreMedical Imaging 2023: Image Processing


ConferenciaMedical Imaging 2023: Image Processing
País/TerritorioEstados Unidos
CiudadSan Diego


Profundice en los temas de investigación de 'An Extensive Pixel-Level Augmentation Framework for Unsupervised Cross-Modality Domain Adaptation'. En conjunto forman una huella única.

Citar esto