RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909389210779648 |
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| author | Chaubey, Gyanendra Farooq, Aiman Singh, Azad Mishra, Deepak |
| author_facet | Chaubey, Gyanendra Farooq, Aiman Singh, Azad Mishra, Deepak |
| contents | The recovery of damaged or resected ribcage structures requires precise, custom-designed implants to restore the integrity and functionality of the thoracic cavity. Traditional implant design methods rely mainly on manual processes, making them time-consuming and susceptible to variability. In this work, we explore the feasibility of automated ribcage implant generation using deep learning. We present a framework based on 3D U-Net architecture that processes CT scans to generate patient-specific implant designs. To the best of our knowledge, this is the first investigation into automated thoracic implant generation using deep learning approaches. Our preliminary results, while moderate, highlight both the potential and the significant challenges in this complex domain. These findings establish a foundation for future research in automated ribcage reconstruction and identify key technical challenges that need to be addressed for practical implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09204 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation Chaubey, Gyanendra Farooq, Aiman Singh, Azad Mishra, Deepak Image and Video Processing Artificial Intelligence Medical Physics The recovery of damaged or resected ribcage structures requires precise, custom-designed implants to restore the integrity and functionality of the thoracic cavity. Traditional implant design methods rely mainly on manual processes, making them time-consuming and susceptible to variability. In this work, we explore the feasibility of automated ribcage implant generation using deep learning. We present a framework based on 3D U-Net architecture that processes CT scans to generate patient-specific implant designs. To the best of our knowledge, this is the first investigation into automated thoracic implant generation using deep learning approaches. Our preliminary results, while moderate, highlight both the potential and the significant challenges in this complex domain. These findings establish a foundation for future research in automated ribcage reconstruction and identify key technical challenges that need to be addressed for practical implementation. |
| title | RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation |
| topic | Image and Video Processing Artificial Intelligence Medical Physics |
| url | https://arxiv.org/abs/2411.09204 |