Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching

TL;DR

This paper introduces an unsupervised domain adaptation method for medical image segmentation that uses feature-space density matching to improve generalization across different datasets without requiring target annotations.

Contribution

The method leverages kernel density estimation to align target and source feature distributions, especially effective with limited target data, advancing unsupervised adaptation in medical imaging.

Findings

Effective with only 3% of target data samples

Improves segmentation accuracy across different datasets

Applicable to MRI and histopathology images

Abstract

Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classified into one or more predefined semantically meaningful classes. Deep learning approaches for semantic segmentation rely on harnessing the power of annotated images to learn features indicative of these semantic classes. Nonetheless, they often fail to generalize when there is a significant domain (i.e., distributional) shift between the training (i.e., source) data and the dataset(s) encountered when deployed (i.e., target), necessitating manual annotations for the target data to achieve acceptable performance. This is especially important in medical imaging because different image modalities have significant intra- and inter-site variations due to protocol and vendor variability. Current techniques are sensitive to hyperparameter tuning and target dataset size. This paper presents an unsupervised domain adaptation approach for semantic segmentation that alleviates the need for annotating target data. Using kernel density estimation, we match the target data distribution to the source in the feature space, particularly when the number of target samples is limited (3% of the target dataset size). We demonstrate the efficacy of our proposed approach on 2 datasets, multisite prostate MRI and histopathology images.