Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917148103802880 |
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| author | Ndir, Tidiane Camaret Pfefferle, Alexander Schirrmeister, Robin Tibor |
| author_facet | Ndir, Tidiane Camaret Pfefferle, Alexander Schirrmeister, Robin Tibor |
| contents | Interactive 3D biomedical image segmentation requires efficient models that can iteratively refine predictions based on user prompts. Current foundation models either lack volumetric awareness or suffer from limited interactive capabilities. We propose a training strategy that combines dynamic volumetric prompt generation with content-aware adaptive cropping to optimize the use of the image encoder. Our method simulates realistic user interaction patterns during training while addressing the computational challenges of learning from sequential refinement feedback on a single GPU. For efficient training, we initialize our network using the publicly available weights from the nnInteractive segmentation model. Evaluation on the \textbf{Foundation Models for Interactive 3D Biomedical Image Segmentation} competition demonstrates strong performance with an average final Dice score of 0.6385, normalized surface distance of 0.6614, and area-under-the-curve metrics of 2.4799 (Dice) and 2.5671 (NSD). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_03189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training Ndir, Tidiane Camaret Pfefferle, Alexander Schirrmeister, Robin Tibor Computer Vision and Pattern Recognition Interactive 3D biomedical image segmentation requires efficient models that can iteratively refine predictions based on user prompts. Current foundation models either lack volumetric awareness or suffer from limited interactive capabilities. We propose a training strategy that combines dynamic volumetric prompt generation with content-aware adaptive cropping to optimize the use of the image encoder. Our method simulates realistic user interaction patterns during training while addressing the computational challenges of learning from sequential refinement feedback on a single GPU. For efficient training, we initialize our network using the publicly available weights from the nnInteractive segmentation model. Evaluation on the \textbf{Foundation Models for Interactive 3D Biomedical Image Segmentation} competition demonstrates strong performance with an average final Dice score of 0.6385, normalized surface distance of 0.6614, and area-under-the-curve metrics of 2.4799 (Dice) and 2.5671 (NSD). |
| title | Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.03189 |