Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection
Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the s...
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| Veröffentlicht in: | Bioengineering (Basel) Jg. 11; H. 11; S. 1082 |
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| Abstract | Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique. |
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| AbstractList | Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique. Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique.Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique. |
| Audience | Academic |
| Author | Lee, Hyung Rae Kim, Hyoungmin Rhee, Wounsuk Chang, Sam Yeol Chang, Bong-Soon |
| AuthorAffiliation | 2 Ministry of Health and Welfare, Government of the Republic of Korea, Sejong 30113, Republic of Korea; rhee1998@snu.ac.kr 3 Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; sam310@seoul.ac.kr (S.Y.C.); bschang@snu.ac.kr (B.-S.C.) 1 Department of Orthopedic Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea; drhrleeos@gmail.com |
| AuthorAffiliation_xml | – name: 1 Department of Orthopedic Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea; drhrleeos@gmail.com – name: 3 Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; sam310@seoul.ac.kr (S.Y.C.); bschang@snu.ac.kr (B.-S.C.) – name: 2 Ministry of Health and Welfare, Government of the Republic of Korea, Sejong 30113, Republic of Korea; rhee1998@snu.ac.kr |
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| Cites_doi | 10.1371/journal.pone.0262689 10.1109/TPAMI.2018.2844175 10.14444/8038 10.1016/j.compbiomed.2021.104384 10.31616/asj.2021.0527 10.1186/s13018-018-0725-1 10.1007/978-3-319-24574-4_28 10.1007/s00464-023-10524-w 10.1007/s10278-022-00629-4 10.1007/s43465-024-01134-2 10.31616/asj.2018.0210 10.1109/ICCV.2019.00612 10.1109/ICCV.2019.00223 10.1109/TPAMI.2017.2699184 10.1109/LGRS.2018.2802944 10.31616/asj.2022.0366 10.1007/978-3-030-33128-3 10.1016/j.wneu.2020.01.080 10.1007/978-3-319-66179-7_65 10.1109/ACCESS.2021.3086020 10.1109/ICCV.2017.322 10.1109/TPAMI.2016.2572683 |
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| Keywords | deep learning endoscopic spine surgery image segmentation computer vision neural tissue |
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| References | Zhang (ref_25) 2018; 15 ref_14 Kim (ref_3) 2018; 13 ref_13 ref_12 Niha (ref_15) 2024; 74 ref_16 Kim (ref_2) 2019; 13 He (ref_18) 2020; 42 Park (ref_6) 2020; 136 Ryu (ref_7) 2024; 38 Chen (ref_10) 2018; 40 Chris (ref_20) 2019; 6 Kundu (ref_19) 2022; 35 Lewandrowski (ref_5) 2021; 15 Shelhamer (ref_11) 2017; 39 Kwon (ref_4) 2022; 16 ref_24 ref_23 ref_22 Junjie (ref_1) 2023; 17 ref_21 ref_27 ref_9 Siddique (ref_17) 2021; 9 ref_8 Bu (ref_26) 2024; 58 |
| References_xml | – ident: ref_9 doi: 10.1371/journal.pone.0262689 – volume: 42 start-page: 386 year: 2020 ident: ref_18 article-title: Mask R-CNN publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2844175 – volume: 15 start-page: 280 year: 2021 ident: ref_5 article-title: Dural Tears During Lumbar Spinal Endoscopy: Surgeon Skill, Training, Incidence, Risk Factors, and Management publication-title: Int. J. Spine Surg. doi: 10.14444/8038 – ident: ref_14 doi: 10.1016/j.compbiomed.2021.104384 – volume: 17 start-page: 418 year: 2023 ident: ref_1 article-title: Comparison of Unilateral Biportal Endoscopy Decompression and Microscopic Decompression Effectiveness in Lumbar Spinal Stenosis Treatment: A Systematic Review and Meta-analysis publication-title: Asian Spine J. doi: 10.31616/asj.2021.0527 – volume: 13 start-page: 22 year: 2018 ident: ref_3 article-title: Clinical comparison of unilateral biportal endoscopic technique versus open microdiscectomy for single-level lumbar discectomy: A multicenter, retrospective analysis publication-title: J. Orthop. Surg. Res. doi: 10.1186/s13018-018-0725-1 – ident: ref_16 doi: 10.1007/978-3-319-24574-4_28 – volume: 38 start-page: 171 year: 2024 ident: ref_7 article-title: Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy publication-title: Surg. Endosc. doi: 10.1007/s00464-023-10524-w – volume: 35 start-page: 1111 year: 2022 ident: ref_19 article-title: Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives publication-title: J. Digit. Imaging doi: 10.1007/s10278-022-00629-4 – volume: 74 start-page: S-5 year: 2024 ident: ref_15 article-title: An Artificial Intelligence model for implant segmentation on periapical radiographs publication-title: J. Pak. Med. Assoc. – volume: 58 start-page: 587 year: 2024 ident: ref_26 article-title: A Multi-Element Identification System Based on Deep Learning for the Visual Field of Percutaneous Endoscopic Spine Surgery publication-title: Indian J. Orthop. doi: 10.1007/s43465-024-01134-2 – volume: 13 start-page: 334 year: 2019 ident: ref_2 article-title: Biportal Endoscopic Spinal Surgery for Lumbar Spinal Stenosis publication-title: Asian Spine J. doi: 10.31616/asj.2018.0210 – ident: ref_23 – ident: ref_21 – ident: ref_24 doi: 10.1109/ICCV.2019.00612 – ident: ref_27 doi: 10.1109/ICCV.2019.00223 – volume: 40 start-page: 834 year: 2018 ident: ref_10 article-title: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2699184 – volume: 15 start-page: 749 year: 2018 ident: ref_25 article-title: Road Extraction by Deep Residual U-Net publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2802944 – volume: 16 start-page: 789 year: 2022 ident: ref_4 article-title: Lumbar Spinal Stenosis: Review Update 2022 publication-title: Asian Spine J. doi: 10.31616/asj.2022.0366 – ident: ref_8 doi: 10.1007/978-3-030-33128-3 – volume: 136 start-page: e578 year: 2020 ident: ref_6 article-title: Dural Tears in Percutaneous Biportal Endoscopic Spine Surgery: Anatomical Location and Management publication-title: World Neurosurg. doi: 10.1016/j.wneu.2020.01.080 – ident: ref_13 doi: 10.1007/978-3-319-66179-7_65 – volume: 9 start-page: 82031 year: 2021 ident: ref_17 article-title: U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3086020 – ident: ref_12 doi: 10.1109/ICCV.2017.322 – ident: ref_22 – volume: 39 start-page: 640 year: 2017 ident: ref_11 article-title: Fully Convolutional Networks for Semantic Segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2572683 – volume: 6 start-page: 014006 year: 2019 ident: ref_20 article-title: Recurrent residual U-Net for medical image segmentation publication-title: J. Med. Imaging |
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| SubjectTerms | Back surgery Bone surgery computer vision Datasets Deep learning Effectiveness endoscopic spine surgery Endoscopy Equipment and supplies Frames (data processing) Frames per second Image enhancement Image processing Image segmentation Injuries Learning curves Medical imaging Neural networks neural tissue Patients Performance evaluation Recall Segmentation Spine Surgeons Surgery Training |
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| Title | Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection |
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