UNIVERS: Learning UNIversal Voxel-wise Embedding via Randomized Synthesis for generalizable one-shot landmark localization

Key points

• This motivates research on cross-modality embedding learning using paired multi-modality images.1
• To address these limitations, we propose UNIVERS, an end-to-end trainable framework that learns UNIversal Voxel-wise Embedding via Randomized Synthesis.1
• Our framework leverages diverse pseudo-data synthesized from organ shape templates, with randomization applied to semantic layout, visual appearance, FOV, and spatial pose.1
• In this study, the efficacy of the proposed method is demonstrated by applying it to the task of localizing the adrenal glands, which are paired, small, and irregular endocrine organs.1

Sources (1)

• [1]UNIVERS: Learning UNIversal Voxel-wise Embedding via Randomized Synthesis for generalizable one-shot landmark localization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:42 AM

  One-shot landmark localization in volume scans with varying fields of view (FOVs) is challenging.

  This motivates research on cross-modality embedding learning using paired multi-modality images.

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