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Uterine disorders, such as adenomyosis and fibroids, are major contributors to pelvic pain, abnormal uterine bleeding, and infertility. Morphologic configurations and geometric alterations of the uterine cavity serve as critical imaging biomarkers in clinical diagnosis. One well established example is the question mark sign, a highly specific indicator of adenomyosis, characterized by distinctive uterine contour distortions. However, beyond this singular marker, a broader range of shape variations may hold diagnostic significance. To systematically capture these morphologic and geometric patterns, we adapted a Variational Autoencoder (VAE) pre-trained on fastMRI datasets. Instead of encoding MRI images alone, we designed the model to jointly incorporate both the segmented uterine cavity and MRI scans. By embedding an anatomy-informed prior, the model is better equipped to characterize structural anatomy relevant to uterine pathology. Our results indicate that both fine-tuning the VAE and using the hybrid encoding approach produce embeddings that align more closely with clinically relevant disease patterns and improve downstream clustering performance. By refining the joint representation of segmentation and MRI data, our method could enhance the potential of latent diffusion models for extracting imaging biomarkers in female pelvic disorders.

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UteroVAE: A Shape-Informed Variational Autoencoder for Uterine MRI Encoding in Adenomyosis, Fibroids, and Healthy Uteri

  • 会议论文

  • 发表日期：

    2026年

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  • Richard Ruppel,
  • Maximilian Lindholz,
  • Robin Schmidt,
  • Yasmin El-Nahry,
  • Sylvia Mechsner,
  • Sophia Elisabeth Ellen Schulze-Weddige,
  • Georg Lukas Baumgärtner,
  • Tillmann Arlt,
  • Charlie Alexander Hamm,
  • Sebastian Arndt,
  • Lisa Siegler,
  • Leonard Stepansky,
  • Jana Hutter,
  • Matthias May,
  • Tobias Penzkofer

摘要

Uterine disorders, such as adenomyosis and fibroids, are major contributors to pelvic pain, abnormal uterine bleeding, and infertility. Morphologic configurations and geometric alterations of the uterine cavity serve as critical imaging biomarkers in clinical diagnosis. One well established example is the question mark sign, a highly specific indicator of adenomyosis, characterized by distinctive uterine contour distortions. However, beyond this singular marker, a broader range of shape variations may hold diagnostic significance. To systematically capture these morphologic and geometric patterns, we adapted a Variational Autoencoder (VAE) pre-trained on fastMRI datasets. Instead of encoding MRI images alone, we designed the model to jointly incorporate both the segmented uterine cavity and MRI scans. By embedding an anatomy-informed prior, the model is better equipped to characterize structural anatomy relevant to uterine pathology. Our results indicate that both fine-tuning the VAE and using the hybrid encoding approach produce embeddings that align more closely with clinically relevant disease patterns and improve downstream clustering performance. By refining the joint representation of segmentation and MRI data, our method could enhance the potential of latent diffusion models for extracting imaging biomarkers in female pelvic disorders.