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

arXiv cs.CV / 3/13/2026

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Key Points

The paper introduces PCA-Enhanced Probabilistic U-Net (PEP U-Net) for Ambiguous Medical Image Segmentation to better handle inherent uncertainties from image ambiguities, noise, and subjective annotations.
The method uses PCA to reduce dimensionality in the posterior network, mitigating redundancy and improving computational efficiency, and employs an inverse PCA operation to reconstruct critical information and enhance latent space capacity.
Compared with conventional generative models, PEP U-Net preserves diverse segmentation hypotheses while achieving a better balance between segmentation accuracy and predictive variability.
The work advances generative modeling in medical image segmentation by combining PCA-based efficiency with improved representational power, enabling more robust and varied segmentation outputs.

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 (\textbf{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.