# Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

Source: [Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training](<https://www.sota2.com/research/paper/68fa6aef1e209131cea2b2b0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:36:16.560Z

Authors: Hexin Dong, Fei Yu, Jie Zhao, Bin Dong, Li Zhang

Published: 2021-09-29T06:56:57.000Z

## Abstract

This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, helping to reduce domain shift in the training segmentation model. Self-training adapts the decision boundary of the segmentation network to fit the distribution of hrT2 scans. Experiment results show that PAST has outperformed the non-UDA baseline significantly, and it received rank-2 on CrossMoDA validation phase Leaderboard with a mean Dice score of 0.8395.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2109.14219>)

[PDF](<https://arxiv.org/pdf/2109.14219.pdf>)