An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus
Paranasal sinus surgery, a common treatment for chronic rhinosinusitis, requires exceptional precision due to the proximity of critical anatomical structures. To ensure accurate instrument control and clear visualization of the surgical site, surgeons utilize computer-aided surgical navigation (CSN)...
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MDPI AG
2024-12-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/1/5 |
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| author | Suhyeon Kim Hyeonji Kim Younhyun Jung |
| author_facet | Suhyeon Kim Hyeonji Kim Younhyun Jung |
| author_sort | Suhyeon Kim |
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| description | Paranasal sinus surgery, a common treatment for chronic rhinosinusitis, requires exceptional precision due to the proximity of critical anatomical structures. To ensure accurate instrument control and clear visualization of the surgical site, surgeons utilize computer-aided surgical navigation (CSN). A key component of CSN is the registration process, which is traditionally reliant on manual or marker-based techniques. However, there is a growing shift toward marker-less registration methods. In previous work, we investigated a mesh-based registration approach using a Mixed Reality Head-Mounted Display (MR-HMD), specifically the Microsoft HoloLens 2. However, this method faced limitations, including depth holes and invalid values. These issues stemmed from the device’s low-resolution camera specifications and the 3D projection steps required to upscale RGB camera spaces. In this study, we propose a novel automated marker-less registration method leveraging Neural Radiance Field (NeRF) technology with an MR-HMD. To address insufficient depth information in the previous approach, we utilize rendered-depth images generated by the trained NeRF model. We evaluated our method against two other techniques, including prior mesh-based registration, using a facial phantom and three participants. The results demonstrate our proposed method achieves at least a 0.873 mm (12%) improvement in registration accuracy compared to others. |
| format | Article |
| id | doaj-art-27df686048ba4c08b1cf9b51c9c406b4 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Computers |
| spelling | doaj-art-27df686048ba4c08b1cf9b51c9c406b42025-01-24T13:27:51ZengMDPI AGComputers2073-431X2024-12-01141510.3390/computers14010005An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal SinusSuhyeon Kim0Hyeonji Kim1Younhyun Jung2School of Computing, Gachon University, Seongnam 13120, Republic of KoreaSchool of Computing, Gachon University, Seongnam 13120, Republic of KoreaSchool of Computing, Gachon University, Seongnam 13120, Republic of KoreaParanasal sinus surgery, a common treatment for chronic rhinosinusitis, requires exceptional precision due to the proximity of critical anatomical structures. To ensure accurate instrument control and clear visualization of the surgical site, surgeons utilize computer-aided surgical navigation (CSN). A key component of CSN is the registration process, which is traditionally reliant on manual or marker-based techniques. However, there is a growing shift toward marker-less registration methods. In previous work, we investigated a mesh-based registration approach using a Mixed Reality Head-Mounted Display (MR-HMD), specifically the Microsoft HoloLens 2. However, this method faced limitations, including depth holes and invalid values. These issues stemmed from the device’s low-resolution camera specifications and the 3D projection steps required to upscale RGB camera spaces. In this study, we propose a novel automated marker-less registration method leveraging Neural Radiance Field (NeRF) technology with an MR-HMD. To address insufficient depth information in the previous approach, we utilize rendered-depth images generated by the trained NeRF model. We evaluated our method against two other techniques, including prior mesh-based registration, using a facial phantom and three participants. The results demonstrate our proposed method achieves at least a 0.873 mm (12%) improvement in registration accuracy compared to others.https://www.mdpi.com/2073-431X/14/1/5marker-less registrationneural radiance fieldsmixed reality |
| spellingShingle | Suhyeon Kim Hyeonji Kim Younhyun Jung An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus Computers marker-less registration neural radiance fields mixed reality |
| title | An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus |
| title_full | An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus |
| title_fullStr | An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus |
| title_full_unstemmed | An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus |
| title_short | An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus |
| title_sort | automated marker less registration approach using neural radiance fields for potential use in mixed reality based computer aided surgical navigation of paranasal sinus |
| topic | marker-less registration neural radiance fields mixed reality |
| url | https://www.mdpi.com/2073-431X/14/1/5 |
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