End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916950105391104 |
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| author | Zarin, Farahdiba Padoy, Nicolas Dana, Jérémy Srivastav, Vinkle |
| author_facet | Zarin, Farahdiba Padoy, Nicolas Dana, Jérémy Srivastav, Vinkle |
| contents | The fine-grained surface reconstruction of different organs from 3D medical imaging can provide advanced diagnostic support and improved surgical planning. However, the representation of the organs is often limited by the resolution, with a detailed higher resolution requiring more memory and computing footprint. Implicit representations of objects have been proposed to alleviate this problem in general computer vision by providing compact and differentiable functions to represent the 3D object shapes. However, architectural and data-related differences prevent the direct application of these methods to medical images. This work introduces ImplMORe, an end-to-end deep learning method using implicit surface representations for multi-organ reconstruction from 3D medical images. ImplMORe incorporates local features using a 3D CNN encoder and performs multi-scale interpolation to learn the features in the continuous domain using occupancy functions. We apply our method for single and multiple organ reconstructions using the totalsegmentator dataset. By leveraging the continuous nature of occupancy functions, our approach outperforms the discrete explicit representation based surface reconstruction approaches, providing fine-grained surface details of the organ at a resolution higher than the given input image. The source code will be made publicly available at: https://github.com/CAMMA-public/ImplMORe |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_12068 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data Zarin, Farahdiba Padoy, Nicolas Dana, Jérémy Srivastav, Vinkle Computer Vision and Pattern Recognition The fine-grained surface reconstruction of different organs from 3D medical imaging can provide advanced diagnostic support and improved surgical planning. However, the representation of the organs is often limited by the resolution, with a detailed higher resolution requiring more memory and computing footprint. Implicit representations of objects have been proposed to alleviate this problem in general computer vision by providing compact and differentiable functions to represent the 3D object shapes. However, architectural and data-related differences prevent the direct application of these methods to medical images. This work introduces ImplMORe, an end-to-end deep learning method using implicit surface representations for multi-organ reconstruction from 3D medical images. ImplMORe incorporates local features using a 3D CNN encoder and performs multi-scale interpolation to learn the features in the continuous domain using occupancy functions. We apply our method for single and multiple organ reconstructions using the totalsegmentator dataset. By leveraging the continuous nature of occupancy functions, our approach outperforms the discrete explicit representation based surface reconstruction approaches, providing fine-grained surface details of the organ at a resolution higher than the given input image. The source code will be made publicly available at: https://github.com/CAMMA-public/ImplMORe |
| title | End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.12068 |