PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910051395960832 |
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| author | Li, Xiangyu Wang, Chenglin Shen, Qiantong Li, Fanding Wang, Wei Wang, Kuanquan Shen, Yi Zhao, Baochun Luo, Gongning |
| author_facet | Li, Xiangyu Wang, Chenglin Shen, Qiantong Li, Fanding Wang, Wei Wang, Kuanquan Shen, Yi Zhao, Baochun Luo, Gongning |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11550 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation Li, Xiangyu Wang, Chenglin Shen, Qiantong Li, Fanding Wang, Wei Wang, Kuanquan Shen, Yi Zhao, Baochun Luo, Gongning Computer Vision and Pattern Recognition 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. |
| title | PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.11550 |