MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
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| _version_ | 1866908960882163712 |
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| author | Marzol, Kacper Kolton, Ignacy Smolak-Dyżewska, Weronika Kaleta, Joanna Świderska-Chadaj, Żaneta Mazur, Marcin Dziekiewicz, Mirosław Markiewicz, Tomasz Spurek, Przemysław |
| author_facet | Marzol, Kacper Kolton, Ignacy Smolak-Dyżewska, Weronika Kaleta, Joanna Świderska-Chadaj, Żaneta Mazur, Marcin Dziekiewicz, Mirosław Markiewicz, Tomasz Spurek, Przemysław |
| contents | Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstruction and novel-view synthesis from endoscopic images are promising tools for enhancing diagnosis. Moreover, integrating physiological deformations and interaction with the endoscope enables the development of simulation tools from real video data. However, constrained camera trajectories and view-dependent lighting create artifacts, leading to inaccurate or overfitted reconstructions. We present MedGS, a novel 3D reconstruction framework leveraging the unique property of endoscopic imaging, where a single light source is closely aligned with the camera. Our method separates light effects from tissue properties. MedGS enhances 3D Gaussian Splatting with a physically based relightable model. We boost the traditional light transport formulation with a specialized MLP capturing complex light-related effects while ensuring reduced artifacts and better generalization across novel views. MedGS achieves superior reconstruction quality compared to baseline methods on both public and in-house datasets. Unlike existing approaches, MedGS enables tissue modifications while preserving a physically accurate response to light, making it closer to real-world clinical use. Repository: https://github.com/gmum/MedGS |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16806 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging Marzol, Kacper Kolton, Ignacy Smolak-Dyżewska, Weronika Kaleta, Joanna Świderska-Chadaj, Żaneta Mazur, Marcin Dziekiewicz, Mirosław Markiewicz, Tomasz Spurek, Przemysław Computer Vision and Pattern Recognition Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstruction and novel-view synthesis from endoscopic images are promising tools for enhancing diagnosis. Moreover, integrating physiological deformations and interaction with the endoscope enables the development of simulation tools from real video data. However, constrained camera trajectories and view-dependent lighting create artifacts, leading to inaccurate or overfitted reconstructions. We present MedGS, a novel 3D reconstruction framework leveraging the unique property of endoscopic imaging, where a single light source is closely aligned with the camera. Our method separates light effects from tissue properties. MedGS enhances 3D Gaussian Splatting with a physically based relightable model. We boost the traditional light transport formulation with a specialized MLP capturing complex light-related effects while ensuring reduced artifacts and better generalization across novel views. MedGS achieves superior reconstruction quality compared to baseline methods on both public and in-house datasets. Unlike existing approaches, MedGS enables tissue modifications while preserving a physically accurate response to light, making it closer to real-world clinical use. Repository: https://github.com/gmum/MedGS |
| title | MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.16806 |