{"title":"Quantized feature alignment for unsupervised domain adaptation in abdominal and prostate segmentation","authors":"Yang Wang, Xu Chen, Xiyu Zhang, Dongliang Liu","doi":"10.1016/j.dsp.2025.105580","DOIUrl":null,"url":null,"abstract":"<div><div>Unsupervised Domain Adaptation (UDA) plays a crucial role in medical image segmentation, especially when annotated target domain data is unavailable. Traditional continuous feature alignment methods face challenges due to mini-batch limitations and often fail to capture the full domain distribution. To address these issues, we propose QFASeg-Net, a novel UDA strategy that utilizes quantized feature alignment instead of conventional continuous approaches. Our grouped quantization technique transforms continuous features into discrete representations, constructing a codebook that progressively learns to capture the entire feature distribution of the domain, rather than adapting to the limited distributions within mini-batches. Moreover, we incorporate multi-scale high-order statistical alignment to refine the alignment of quantized features across different domain spaces, enhancing cross-domain feature consistency. Experimental results on abdominal and prostate segmentation tasks demonstrate that QFASeg-Net outperforms existing methods, validating the effectiveness of quantized feature alignment for cross-modality medical image segmentation.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105580"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425006025","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Unsupervised Domain Adaptation (UDA) plays a crucial role in medical image segmentation, especially when annotated target domain data is unavailable. Traditional continuous feature alignment methods face challenges due to mini-batch limitations and often fail to capture the full domain distribution. To address these issues, we propose QFASeg-Net, a novel UDA strategy that utilizes quantized feature alignment instead of conventional continuous approaches. Our grouped quantization technique transforms continuous features into discrete representations, constructing a codebook that progressively learns to capture the entire feature distribution of the domain, rather than adapting to the limited distributions within mini-batches. Moreover, we incorporate multi-scale high-order statistical alignment to refine the alignment of quantized features across different domain spaces, enhancing cross-domain feature consistency. Experimental results on abdominal and prostate segmentation tasks demonstrate that QFASeg-Net outperforms existing methods, validating the effectiveness of quantized feature alignment for cross-modality medical image segmentation.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,