Prompt-Guided Patch UNet-VAE with Adversarial Supervision for Adrenal Gland Segmentation in Computed Tomography Medical Images

• URL: http://arxiv.org/abs/2509.03188v1
• Date: Wed, 03 Sep 2025 10:18:06 GMT
• Title: Prompt-Guided Patch UNet-VAE with Adversarial Supervision for Adrenal Gland Segmentation in Computed Tomography Medical Images
• Authors: Hania Ghouse, Muzammil Behzad,
• Abstract summary: Small abdominal organs, such as the adrenal glands in CT imaging, remains a persistent challenge due to severe class imbalance, poor spatial context, and limited annotated data.<n>We propose a unified framework that combines variational reconstruction, supervised segmentation, and adversarial patch-based feedback to address these limitations in a principled and scalable manner.<n>Our findings highlight the effectiveness of hybrid generative-discriminative training regimes for small-organ segmentation and provide new insights into balancing realism, diversity, and anatomical consistency in data-scarce scenarios.
• Score: 0.3437656066916039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmentation of small and irregularly shaped abdominal organs, such as the adrenal glands in CT imaging, remains a persistent challenge due to severe class imbalance, poor spatial context, and limited annotated data. In this work, we propose a unified framework that combines variational reconstruction, supervised segmentation, and adversarial patch-based feedback to address these limitations in a principled and scalable manner. Our architecture is built upon a VAE-UNet backbone that jointly reconstructs input patches and generates voxel-level segmentation masks, allowing the model to learn disentangled representations of anatomical structure and appearance. We introduce a patch-based training pipeline that selectively injects synthetic patches generated from the learned latent space, and systematically study the effects of varying synthetic-to-real patch ratios during training. To further enhance output fidelity, the framework incorporates perceptual reconstruction loss using VGG features, as well as a PatchGAN-style discriminator for adversarial supervision over spatial realism. Comprehensive experiments on the BTCV dataset demonstrate that our approach improves segmentation accuracy, particularly in boundary-sensitive regions, while maintaining strong reconstruction quality. Our findings highlight the effectiveness of hybrid generative-discriminative training regimes for small-organ segmentation and provide new insights into balancing realism, diversity, and anatomical consistency in data-scarce scenarios.

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