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PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation

  • Harbin Institute of Technology
  • Peking University
  • Harbin Institute of Technology Shenzhen
  • Hainan College of Software Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Ambiguous Medical Image Segmentation (AMIS) is significant to address the challenges of inherent uncertainties from image ambiguities, noise, and subjective annotations. Existing conditional variational autoencoder (cVAE)-based methods effectively capture uncertainty but face limitations including redundancy in high-dimensional latent spaces and limited expressiveness of single posterior networks. To overcome these issues, we introduce a novel PCA-Enhanced Probabilistic U-Net (PEP U-Net). Our method effectively incorporates Principal Component Analysis (PCA) for dimensionality reduction in the posterior network to mitigate redundancy and improve computational efficiency. Additionally, we further employ an inverse PCA operation to reconstruct critical information, enhancing the latent space's representational capacity. Compared to conventional generative models, our method preserves the ability to generate diverse segmentation hypotheses while achieving a superior balance between segmentation accuracy and predictive variability, thereby advancing the performance of generative modeling in medical image segmentation.

Original languageEnglish
Title of host publicationISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
ISBN (Electronic)9798331577636
DOIs
StatePublished - 2026
Event23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026 - London, United Kingdom
Duration: 8 Apr 202611 Apr 2026

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2026-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026
Country/TerritoryUnited Kingdom
CityLondon
Period8/04/2611/04/26

Keywords

  • Ambiguous medical image segmentation
  • principal component analysis
  • uncertainty estimation

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