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Bartholomew RA, Zhou H, Boreel M, Suresh K, Gupta S, Mitchell MB, Hong C, Lee SE, Smith TR, Guenette JP, Corrales CE, Jagadeesan J. Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction. JAMA Otolaryngol Head Neck Surg 2024; 150:318-326. [PMID: 38451508 PMCID: PMC11009826 DOI: 10.1001/jamaoto.2024.0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/07/2024] [Indexed: 03/08/2024]
Abstract
Importance Image guidance is an important adjunct for endoscopic sinus and skull base surgery. However, current systems require bulky external tracking equipment, and their use can interrupt efficient surgical workflow. Objective To evaluate a trackerless surgical navigation system using 3-dimensional (3D) endoscopy and simultaneous localization and mapping (SLAM) algorithms in the anterior skull base. Design, Setting, and Participants This interventional deceased donor cohort study and retrospective clinical case study was conducted at a tertiary academic medical center with human deceased donor specimens and a patient with anterior skull base pathology. Exposures Participants underwent endoscopic endonasal transsphenoidal dissection and surface model reconstruction from stereoscopic video with registration to volumetric models segmented from computed tomography (CT) and magnetic resonance imaging. Main Outcomes and Measures To assess the fidelity of surface model reconstruction and accuracy of surgical navigation and surface-CT model coregistration, 3 metrics were calculated: reconstruction error, registration error, and localization error. Results In deceased donor models (n = 9), high-fidelity surface models of the posterior wall of the sphenoid sinus were reconstructed from stereoscopic video and coregistered to corresponding volumetric CT models. The mean (SD; range) reconstruction, registration, and localization errors were 0.60 (0.24; 0.36-0.93), 1.11 (0.49; 0.71-1.56) and 1.01 (0.17; 0.78-1.25) mm, respectively. In a clinical case study of a patient who underwent a 3D endoscopic endonasal transsphenoidal resection of a tubercular meningioma, a high-fidelity surface model of the posterior wall of the sphenoid was reconstructed from intraoperative stereoscopic video and coregistered to a volumetric preoperative fused CT magnetic resonance imaging model with a root-mean-square error of 1.38 mm. Conclusions and Relevance The results of this study suggest that SLAM algorithm-based endoscopic endonasal surgery navigation is a novel, accurate, and trackerless approach to surgical navigation that uses 3D endoscopy and SLAM-based algorithms in lieu of conventional optical or electromagnetic tracking. While multiple challenges remain before clinical readiness, a SLAM algorithm-based endoscopic endonasal surgery navigation system has the potential to improve surgical efficiency, economy of motion, and safety.
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Affiliation(s)
- Ryan A. Bartholomew
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston
- Division of Otolaryngology–Head and Neck Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Haoyin Zhou
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Maud Boreel
- Division of Otolaryngology–Head and Neck Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Krish Suresh
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston
- Division of Otolaryngology–Head and Neck Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Saksham Gupta
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Margaret B. Mitchell
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston
- Division of Otolaryngology–Head and Neck Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Christopher Hong
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stella E. Lee
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston
- Division of Otolaryngology–Head and Neck Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Timothy R. Smith
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jeffrey P. Guenette
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
| | - C. Eduardo Corrales
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston
- Division of Otolaryngology–Head and Neck Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jayender Jagadeesan
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
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Russe MF, Rebmann P, Tran PH, Kellner E, Reisert M, Bamberg F, Kotter E, Kim S. AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice. BMJ Open 2024; 14:e076954. [PMID: 38262641 PMCID: PMC10823998 DOI: 10.1136/bmjopen-2023-076954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models for fracture detection on musculoskeletal radiographs of the distal radius by aligning their outputs. DESIGN AND SETTING This single-centre retrospective study was conducted on a random subset of emergency department radiographs from 2008 to 2018 of the distal radius in Germany. MATERIALS AND METHODS An image set was created to be compatible with training and testing classification and segmentation models by annotating examinations for fractures and overlaying fracture masks, if applicable. Representative classification and segmentation models were trained on 80% of the data. After output binarisation, their derived fracture detection performances as well as that of a standard commercially available solution were compared on the remaining X-rays (20%) using mainly accuracy and area under the receiver operating characteristic (AUROC). RESULTS A total of 2856 examinations with 712 (24.9%) fractures were included in the analysis. Accuracies reached up to 0.97 for the classification model, 0.94 for the segmentation model and 0.95 for BoneView. Cohen's kappa was at least 0.80 in pairwise comparisons, while Fleiss' kappa was 0.83 for all models. Fracture predictions were visualised with all three methods at different levels of detail, ranking from downsampled image region for classification over bounding box for detection to single pixel-level delineation for segmentation. CONCLUSIONS All three investigated approaches reached high performances for detection of distal radius fractures with simple preprocessing and postprocessing protocols on the custom-trained models. Despite their underlying structural differences, selection of one's fracture analysis AI tool in the frame of this study reduces to the desired flavour of automation: automated classification, AI-assisted manual fracture reading or minimised false negatives.
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Affiliation(s)
- Maximilian Frederik Russe
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Philipp Rebmann
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Phuong Hien Tran
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Elias Kellner
- Department of Medical Physics, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Marco Reisert
- Department of Medical Physics, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Suam Kim
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
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Rau A, Schröter N, Rijntjes M, Bamberg F, Jost WH, Zaitsev M, Weiller C, Rau S, Urbach H, Reisert M, Russe MF. Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy. Eur Radiol 2023; 33:7160-7167. [PMID: 37121929 PMCID: PMC10511621 DOI: 10.1007/s00330-023-09665-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/05/2023] [Accepted: 03/10/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVES The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls. METHODS We retrospectively included patients with MSA and PD as well as healthy controls. A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). The DNP's performance was compared with putaminal segmentations as derived by Automatic Anatomic Labelling, Freesurfer and Fastsurfer. For validation, we assessed the diagnostic accuracy of the resulting segmentations in the delineation of MSA vs. PD and healthy controls. RESULTS A total of 251 subjects (61 patients with MSA, 158 patients with PD, and 32 healthy controls; mean age of 61.5 ± 8.8 years) were included. Compared to the dice-coefficient of the DNP (0.96), we noted significantly weaker performance for AAL3 (0.72; p < .001), Freesurfer (0.82; p < .001), and Fastsurfer (0.84, p < .001). This was corroborated by the superior diagnostic performance of MSA vs. PD and HC of the DNP (AUC 0.93) versus the AUC of 0.88 for AAL3 (p = 0.02), 0.86 for Freesurfer (p = 0.048), and 0.85 for Fastsurfer (p = 0.04). CONCLUSION By utilization of a DNP, accurate segmentations of the putamen can be obtained even if substantial atrophy is present. This allows for more precise extraction of imaging parameters or shape features from the putamen in relevant patient cohorts. CLINICAL RELEVANCE STATEMENT Deep learning-based segmentation of the putamen was superior to currently available algorithms and is beneficial for the diagnosis of multiple system atrophy. KEY POINTS • A Deep Neural Patchwork precisely delineates the putamen and performs equal to human labeling in multiple system atrophy, even when pronounced putaminal volume loss is present. • The Deep Neural Patchwork-based segmentation was more capable to differentiate between multiple system atrophy and Parkinson's disease than the AAL3 atlas, Freesurfer, or Fastsurfer.
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Affiliation(s)
- Alexander Rau
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Nils Schröter
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michel Rijntjes
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Maxim Zaitsev
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Stephan Rau
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maximilian F Russe
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Weingart JV, Schlager S, Metzger MC, Brandenburg LS, Hein A, Schmelzeisen R, Bamberg F, Kim S, Kellner E, Reisert M, Russe MF. Automated detection of cephalometric landmarks using deep neural patchworks. Dentomaxillofac Radiol 2023; 52:20230059. [PMID: 37427585 PMCID: PMC10461263 DOI: 10.1259/dmfr.20230059] [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: 02/02/2023] [Revised: 04/25/2023] [Accepted: 05/13/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVES This study evaluated the accuracy of deep neural patchworks (DNPs), a deep learning-based segmentation framework, for automated identification of 60 cephalometric landmarks (bone-, soft tissue- and tooth-landmarks) on CT scans. The aim was to determine whether DNP could be used for routine three-dimensional cephalometric analysis in diagnostics and treatment planning in orthognathic surgery and orthodontics. METHODS Full skull CT scans of 30 adult patients (18 female, 12 male, mean age 35.6 years) were randomly divided into a training and test data set (each n = 15). Clinician A annotated 60 landmarks in all 30 CT scans. Clinician B annotated 60 landmarks in the test data set only. The DNP was trained using spherical segmentations of the adjacent tissue for each landmark. Automated landmark predictions in the separate test data set were created by calculating the center of mass of the predictions. The accuracy of the method was evaluated by comparing these annotations to the manual annotations. RESULTS The DNP was successfully trained to identify all 60 landmarks. The mean error of our method was 1.94 mm (SD 1.45 mm) compared to a mean error of 1.32 mm (SD 1.08 mm) for manual annotations. The minimum error was found for landmarks ANS 1.11 mm, SN 1.2 mm, and CP_R 1.25 mm. CONCLUSION The DNP-algorithm was able to accurately identify cephalometric landmarks with mean errors <2 mm. This method could improve the workflow of cephalometric analysis in orthodontics and orthognathic surgery. Low training requirements while still accomplishing high precision make this method particularly promising for clinical use.
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Affiliation(s)
- Julia Vera Weingart
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Stefan Schlager
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marc Christian Metzger
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Leonard Simon Brandenburg
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anna Hein
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Rainer Schmelzeisen
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Suam Kim
- Department of Diagnostic and Interventional Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Department of Medical Physics, Faculty of Medicine, Medical Center – University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Department of Medical Physics, Faculty of Medicine, Medical Center – University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Maximilian Frederik Russe
- Department of Diagnostic and Interventional Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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