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

TL;DR

This paper introduces a PCA-enhanced probabilistic U-Net that reduces redundancy in the latent space and improves the quality of ambiguous medical image segmentation by balancing accuracy and variability.

Contribution

It proposes a novel PCA-based dimensionality reduction and inverse PCA reconstruction in a probabilistic U-Net for better uncertainty modeling in medical segmentation.

Findings

Reduces latent space redundancy with PCA

Enhances segmentation diversity and accuracy

Improves computational efficiency in uncertainty modeling

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.