用PCA优化生成模型，提升医学图像分割的不确定性和准确性。
PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation
- 引入PCA降维减少潜空间冗余，提升计算效率
- 通过逆PCA重建关键信息，增强表征能力
- 在多样分割结果与精度间取得更好平衡
模糊医学图像分割（AMIS）旨在应对图像模糊、噪声及主观标注带来的固有不确定性。现有基于条件变分自编码器（cVAE）的方法虽能有效捕捉不确定性，但面临高维潜空间冗余和单个后验网络表达能力有限的问题。为此，本文提出新型PCA增强概率U-Net（PEP U-Net）。该方法在后验网络中引入主成分分析（PCA）进行降维，缓解冗余并提升计算效率；同时通过逆PCA操作重构关键信息，增强潜空间的表征能力。相较于传统生成模型，本方法在保持生成多样化分割假设能力的同时，实现了分割精度与预测可变性之间的更优平衡，推动了生成建模在医学图像分割中的性能提升。
原文摘要 · Abstract (English)
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.
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