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Tu R, Zhang D, Li C, Xiao L, Zhang Y, Cai X, Si W. Multimodal MRI segmentation of key structures for microvascular decompression via knowledge-driven mutual distillation and topological constraints. Int J Comput Assist Radiol Surg 2024; 19:1329-1338. [PMID: 38739324 DOI: 10.1007/s11548-024-03159-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024]
Abstract
PURPOSE Microvascular decompression (MVD) is a widely used neurosurgical intervention for the treatment of cranial nerves compression. Segmentation of MVD-related structures, including the brainstem, nerves, arteries, and veins, is critical for preoperative planning and intraoperative decision-making. Automatically segmenting structures related to MVD is still challenging for current methods due to the limited information from a single modality and the complex topology of vessels and nerves. METHODS Considering that it is hard to distinguish MVD-related structures, especially for nerve and vessels with similar topology, we design a multimodal segmentation network with a shared encoder-dual decoder structure and propose a clinical knowledge-driven distillation scheme, allowing reliable knowledge transferred from each decoder to the other. Besides, we introduce a class-wise contrastive module to learn the discriminative representations by maximizing the distance among classes across modalities. Then, a projected topological loss based on persistent homology is proposed to constrain topological continuity. RESULTS We evaluate the performance of our method on in-house dataset consisting of 100 paired HR-T2WI and 3D TOF-MRA volumes. Experiments indicate that our model outperforms the SOTA in DSC by 1.9% for artery, 3.3% for vein and 0.5% for nerve. Visualization results show our method attains improved continuity and less breakage, which is also consistent with intraoperative images. CONCLUSION Our method can comprehensively extract the distinct features from multimodal data to segment the MVD-related key structures and preserve the topological continuity, allowing surgeons precisely perceiving the patient-specific target anatomy and substantially reducing the workload of surgeons in the preoperative planning stage. Our resources will be publicly available at https://github.com/JaronTu/Multimodal_MVD_Seg .
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Affiliation(s)
- Renzhe Tu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, No.1 Yanqihu East Rd, Beijing, 101408, China
| | - Doudou Zhang
- The Second School of Clinical Medicine, Southern Medical University, No.1023, South Shatai Road, Guangzhou, 510515, China
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, 466 Xingang Middle Road, Guangzhou, 510317, China
- Department of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Sungang West Road 3002, Shenzhen, 518035, China
| | - Caizi Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, 518055, China.
| | - Linxia Xiao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, 518055, China
| | - Yong Zhang
- The Second School of Clinical Medicine, Southern Medical University, No.1023, South Shatai Road, Guangzhou, 510515, China
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, 466 Xingang Middle Road, Guangzhou, 510317, China
| | - Xiaodong Cai
- Department of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Sungang West Road 3002, Shenzhen, 518035, China
| | - Weixin Si
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, 518055, China.
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Kobets AJ, Alavi SAN, Ahmad SJ, Castillo A, Young D, Minuti A, Altschul DJ, Zhu M, Abbott R. Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review. Neurosurg Rev 2024; 47:170. [PMID: 38637466 PMCID: PMC11026186 DOI: 10.1007/s10143-024-02366-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/04/2024] [Accepted: 03/16/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Segmentation tools continue to advance, evolving from manual contouring to deep learning. Researchers have utilized segmentation to study a myriad of posterior fossa-related conditions, such as Chiari malformation, trigeminal neuralgia, post-operative pediatric cerebellar mutism syndrome, and Crouzon syndrome. Herein, we present a summary of the current literature on segmentation of the posterior fossa. The review highlights the various segmentation techniques, and their respective strengths and weaknesses, employed along with objectives and outcomes of the various studies reported in the literature. METHODS A literature search was conducted in PubMed, Embase, Cochrane, and Web of Science up to November 2023 for articles on segmentation techniques of posterior fossa. The two senior authors searched through databases based on the keywords of the article separately and then enrolled joint articles that met the inclusion and exclusion criteria. RESULTS The initial search identified 2205 articles. After applying inclusion and exclusion criteria, 77 articles were selected for full-text review after screening of titles/abstracts. 52 articles were ultimately included in the review. Segmentation techniques included manual, semi-automated, and fully automated (atlas-based, convolutional neural networks). The most common pathology investigated was Chiari malformation. CONCLUSIONS Various forms of segmentation techniques have been used to assess posterior fossa volumes/pathologies and each has its advantages and disadvantages. We discuss these nuances and summarize the current state of literature in the context of posterior fossa-associated pathologies.
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Affiliation(s)
- Andrew J Kobets
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| | - Seyed Ahmad Naseri Alavi
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA.
| | | | | | | | | | - David J Altschul
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| | - Michael Zhu
- Albert Einstein College of Medicine, New York City, USA
| | - Rick Abbott
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
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Cho HJ, Lloyd T, Zammit A, Pattavilakom Sadasivan A, Wagels M, Sutherland A. Radiologically derived 3D virtual models for neurosurgical planning. J Clin Neurosci 2024; 123:23-29. [PMID: 38518385 DOI: 10.1016/j.jocn.2024.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/11/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024]
Abstract
Three dimensional (3D) virtual models for neurosurgery have demonstrated substantial clinical utility, especially for neuro-oncological cases. Computer-aided design (CAD) modelling of radiological images can provide realistic and high-quality 3D models which neurosurgeons may use pre-operatively for surgical planning. 3D virtual models are useful as they are the basis for other models that build off this design. 3D virtual models are quick to segment but can also be easily added to normal neurosurgical and radiological workflow without disruption. Three anatomically complex neuro-oncology cases that were referred from a single institution by three different neurosurgeons were segmented and 3D virtual models were created for pre-operative surgical planning. A face-to-face interview was performed with the surgeons after the models were delivered to gauge the usefulness of the model in pre-surgical planning. All three neurosurgeons found that the 3D virtual model was useful for presurgical planning. Specifically, the virtual model helped in planning operative positioning, understanding spatial relationship between lesion and surrounding critical anatomy and identifying anatomy that will be encountered intra-operatively in a sequential manner. It provided benefit in Multidisciplinary team (MDT) meetings and patient education for shared decision making.3D virtual models are beneficial for pre-surgical planning and patient education for shared decision making for neurosurgical neuro-oncology cases. We believe this could be further expanded to other surgical specialties. The integration of 3D virtual models into normal workflow as the initial step will provide an easier transition into modalities that build off the virtual models such as printed, virtual, augmented and mixed reality models.
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Affiliation(s)
- Hyun-Jae Cho
- Australian Centre for Complex Integration of Surgical Solutions (ACCISS), Woolloongabba, QLD 4102, Australia; Translational Research Institute, Woolloongabba, QLD 4102, Australia; The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia.
| | - Thomas Lloyd
- Australian Centre for Complex Integration of Surgical Solutions (ACCISS), Woolloongabba, QLD 4102, Australia; Translational Research Institute, Woolloongabba, QLD 4102, Australia; The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia; Department of Radiology, The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia
| | - Adrian Zammit
- The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia; Department of Neurosurgery, The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia
| | - Ananthababu Pattavilakom Sadasivan
- The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia; Department of Neurosurgery, The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia
| | - Michael Wagels
- Australian Centre for Complex Integration of Surgical Solutions (ACCISS), Woolloongabba, QLD 4102, Australia; Translational Research Institute, Woolloongabba, QLD 4102, Australia; The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia
| | - Allison Sutherland
- Australian Centre for Complex Integration of Surgical Solutions (ACCISS), Woolloongabba, QLD 4102, Australia; Translational Research Institute, Woolloongabba, QLD 4102, Australia; The Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia
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Bardeesi A, Tabarestani TQ, Bergin SM, Huang CC, Shaffrey CI, Wiggins WF, Abd-El-Barr MM. Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report. J Clin Med 2024; 13:1513. [PMID: 38592365 PMCID: PMC10934424 DOI: 10.3390/jcm13051513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/23/2024] [Accepted: 02/29/2024] [Indexed: 04/10/2024] Open
Abstract
The transfacet minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is a novel approach available for the management of lumbar spondylolisthesis. It avoids the need to manipulate either of the exiting or traversing nerve roots, both protected by the bony boundaries of the approach. With the advancement in operative technologies such as navigation, mapping, segmentation, and augmented reality (AR), surgeons are prompted to utilize these technologies to enhance their surgical outcomes. A 36-year-old male patient was complaining of chronic progressive lower back pain. He was found to have grade 2 L4/5 spondylolisthesis. We studied the feasibility of a trans-Kambin or a transfacet MIS-TLIF, and decided to proceed with the latter given the wider corridor it provides. Preoperative trajectory planning and level segmentation in addition to intraoperative navigation and image merging were all utilized to provide an AR model to guide us through the surgery. The use of AR can build on the safety and learning of novel surgical approaches to spine pathologies. However, larger high-quality studies are needed to further objectively analyze its impact on surgical outcomes and to expand on its application.
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Affiliation(s)
- Anas Bardeesi
- Department of Neurosurgery, Duke University Hospital, Durham, NC 27710, USA
| | | | - Stephen M. Bergin
- Department of Neurosurgery, Duke University Hospital, Durham, NC 27710, USA
| | - Chuan-Ching Huang
- Department of Neurosurgery, Duke University Hospital, Durham, NC 27710, USA
| | | | - Walter F. Wiggins
- Department of Radiology, Duke University Hospital, Durham, NC 27710, USA
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Tabarestani TQ, Salven DS, Sykes DAW, Bardeesi AM, Bartlett AM, Wang TY, Paturu MR, Dibble CF, Shaffrey CI, Ray WZ, Chi JH, Wiggins WF, Abd-El-Barr MM. Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion. Oper Neurosurg (Hagerstown) 2023:01787389-990000000-01010. [PMID: 38149852 DOI: 10.1227/ons.0000000000001046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/15/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND AND OBJECTIVES There has been a rise in minimally invasive methods to access the intervertebral disk space posteriorly given their decreased tissue destruction, lower blood loss, and earlier return to work. Two such options include the percutaneous lumbar interbody fusion through the Kambin triangle and the endoscopic transfacet approach. However, without accurate preoperative visualization, these approaches carry risks of damaging surrounding structures, especially the nerve roots. Using novel segmentation technology, our goal was to analyze the anatomic borders and relative sizes of the safe triangle, trans-Kambin, and the transfacet corridors to assist surgeons in planning a safe approach and determining cannula diameters. METHODS The areas of the safe triangle, Kambin, and transfacet corridors were measured using commercially available software (BrainLab, Munich, Germany). For each approach, the exiting nerve root, traversing nerve roots, theca, disk, and vertebrae were manually segmented on 3-dimensional T2-SPACE magnetic resonance imaging using a region-growing algorithm. The triangles' borders were delineated ensuring no overlap between the area and the nerves. RESULTS A total of 11 patients (65.4 ± 12.5 years, 33.3% female) were retrospectively reviewed. The Kambin, safe, and transfacet corridors were measured bilaterally at the operative level. The mean area (124.1 ± 19.7 mm2 vs 83.0 ± 11.7 mm2 vs 49.5 ± 11.4 mm2) and maximum permissible cannula diameter (9.9 ± 0.7 mm vs 6.8 ± 0.5 mm vs 6.05 ± 0.7 mm) for the transfacet triangles were significantly larger than Kambin and the traditional safe triangles, respectively (P < .001). CONCLUSION We identified, in 3-dimensional, the borders for the transfacet corridor: the traversing nerve root extending inferiorly until the caudal pedicle, the theca medially, and the exiting nerve root superiorly. These results illustrate the utility of preoperatively segmenting anatomic landmarks, specifically the nerve roots, to help guide decision-making when selecting the optimal operative approach.
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Affiliation(s)
- Troy Q Tabarestani
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - David S Salven
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - David A W Sykes
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Anas M Bardeesi
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | - Alyssa M Bartlett
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Timothy Y Wang
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | - Mounica R Paturu
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | - Christopher F Dibble
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | | | - Wilson Z Ray
- Department of Neurosurgery, Washington University, St. Louis, Missouri, USA
| | - John H Chi
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Walter F Wiggins
- Department of Radiology, Duke University Hospital, Durham, North Carolina, USA
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Battistelli M, Izzo A, D’Ercole M, D’Alessandris QG, Montano N. The role of artificial intelligence in the management of trigeminal neuralgia. Front Surg 2023; 10:1310414. [PMID: 38033529 PMCID: PMC10687176 DOI: 10.3389/fsurg.2023.1310414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Trigeminal neuralgia (TN) is the most frequent facial pain. It is difficult to treat pharmacologically and a significant amount of patients can become drug-resistant requiring surgical intervention. From an etiologically point of view TN can be distinguished in a classic form, usually due to a neurovascular conflict, a secondary form (for example related to multiple sclerosis or a cerebello-pontine angle tumor) and an idiopathic form in which no anatomical cause is identifiable. Despite numerous efforts to treat TN, many patients experience recurrence after multiple operations. This fact reflects our incomplete understanding of TN pathogenesis. Artificial intelligence (AI) uses computer technology to develop systems for extension of human intelligence. In the last few years, it has been a widespread of AI in different areas of medicine to implement diagnostic accuracy, treatment selection and even drug production. The aim of this mini-review is to provide an up to date of the state-of-art of AI applications in TN diagnosis and management.
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Affiliation(s)
| | | | | | | | - Nicola Montano
- Department of Neuroscience, Neurosurgery Section, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
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Zhang C, Li M, Luo Z, Xiao R, Li B, Shi J, Zeng C, Sun B, Xu X, Yang H. Deep learning-driven MRI trigeminal nerve segmentation with SEVB-net. Front Neurosci 2023; 17:1265032. [PMID: 37920295 PMCID: PMC10618361 DOI: 10.3389/fnins.2023.1265032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 09/29/2023] [Indexed: 11/04/2023] Open
Abstract
Purpose Trigeminal neuralgia (TN) poses significant challenges in its diagnosis and treatment due to its extreme pain. Magnetic resonance imaging (MRI) plays a crucial role in diagnosing TN and understanding its pathogenesis. Manual delineation of the trigeminal nerve in volumetric images is time-consuming and subjective. This study introduces a Squeeze and Excitation with BottleNeck V-Net (SEVB-Net), a novel approach for the automatic segmentation of the trigeminal nerve in three-dimensional T2 MRI volumes. Methods We enrolled 88 patients with trigeminal neuralgia and 99 healthy volunteers, dividing them into training and testing groups. The SEVB-Net was designed for end-to-end training, taking three-dimensional T2 images as input and producing a segmentation volume of the same size. We assessed the performance of the basic V-Net, nnUNet, and SEVB-Net models by calculating the Dice similarity coefficient (DSC), sensitivity, precision, and network complexity. Additionally, we used the Mann-Whitney U test to compare the time required for manual segmentation and automatic segmentation with manual modification. Results In the testing group, the experimental results demonstrated that the proposed method achieved state-of-the-art performance. SEVB-Net combined with the ωDoubleLoss loss function achieved a DSC ranging from 0.6070 to 0.7923. SEVB-Net combined with the ωDoubleLoss method and nnUNet combined with the DoubleLoss method, achieved DSC, sensitivity, and precision values exceeding 0.7. However, SEVB-Net significantly reduced the number of parameters (2.20 M), memory consumption (11.41 MB), and model size (17.02 MB), resulting in improved computation and forward time compared with nnUNet. The difference in average time between manual segmentation and automatic segmentation with manual modification for both radiologists was statistically significant (p < 0.001). Conclusion The experimental results demonstrate that the proposed method can automatically segment the root and three main branches of the trigeminal nerve in three-dimensional T2 images. SEVB-Net, compared with the basic V-Net model, showed improved segmentation performance and achieved a level similar to nnUNet. The segmentation volumes of both SEVB-Net and nnUNet aligned with expert annotations but SEVB-Net displayed a more lightweight feature.
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Affiliation(s)
- Chuan Zhang
- The First Affiliated Hospital, Jinan University, Guangzhou, China
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Man Li
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Zheng Luo
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ruhui Xiao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Bing Li
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing Shi
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Chen Zeng
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - BaiJinTao Sun
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaoxue Xu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Hanfeng Yang
- The First Affiliated Hospital, Jinan University, Guangzhou, China
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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