UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

by   Jianghao Wu, et al.

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled. We propose a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical image segmentation. Specifically, we propose Target Domain Growing (TDG) to enhance the diversity of predictions in the target domain by duplicating the pre-trained model's prediction head multiple times with perturbations. The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS) strategy that uses reliable pseudo labels obtained in one forward pass to supervise predictions in the next forward pass. The adaptation is further regularized by a mean prediction-based entropy minimization term that encourages confident and consistent results in different prediction heads. UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a cross-modality fetal brain segmentation dataset, and a 3D fetal tissue segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89 percentage points for the three tasks compared with the baseline, respectively, and outperformed several state-of-the-art SFDA methods.


page 1

page 3

page 8

page 10

page 11


ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation

The domain discrepancy existed between medical images acquired in differ...

3D Masked Autoencoding and Pseudo-labeling for Domain Adaptive Segmentation of Heterogeneous Infant Brain MRI

Robust segmentation of infant brain MRI across multiple ages, modalities...

ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training

Unsupervised domain adaptation (UDA) has been vastly explored to allevia...

Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation

This work addresses the challenging domain adaptation setting in which k...

An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation

With recent advances in supervised machine learning for medical image an...

Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training

Models capable of leveraging unlabelled data are crucial in overcoming l...

Source-Relaxed Domain Adaptation for Image Segmentation

Domain adaptation (DA) has drawn high interests for its capacity to adap...

Please sign up or login with your details

Forgot password? Click here to reset