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Fishman Z, Mainprize JG, Edwards G, Antonyshyn O, Hardisty M, Whyne CM. Thickness and design features of clinical cranial implants-what should automated methods strive to replicate? Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03068-4. [PMID: 38430381 DOI: 10.1007/s11548-024-03068-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 01/24/2024] [Indexed: 03/03/2024]
Abstract
PURPOSE New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use. METHODS Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a 'rim' was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified. RESULTS The average thickness of the clinically used implants was 6.0 ± 0.5 mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of -0.3 ± 1.4 mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0 mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2 mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4 mm) were used to cap and create a smooth boundary with the skull. CONCLUSIONS For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.
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Affiliation(s)
- Z Fishman
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada.
| | | | | | - Oleh Antonyshyn
- Calavera Surgical Design Inc., Toronto, ON, Canada
- Division of Plastic Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Michael Hardisty
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - C M Whyne
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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2
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Wu CT, Yang YH, Chang YZ. Creating high-resolution 3D cranial implant geometry using deep learning techniques. Front Bioeng Biotechnol 2023; 11:1297933. [PMID: 38149174 PMCID: PMC10750412 DOI: 10.3389/fbioe.2023.1297933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/22/2023] [Indexed: 12/28/2023] Open
Abstract
Creating a personalized implant for cranioplasty can be costly and aesthetically challenging, particularly for comminuted fractures that affect a wide area. Despite significant advances in deep learning techniques for 2D image completion, generating a 3D shape inpainting remains challenging due to the higher dimensionality and computational demands for 3D skull models. Here, we present a practical deep-learning approach to generate implant geometry from defective 3D skull models created from CT scans. Our proposed 3D reconstruction system comprises two neural networks that produce high-quality implant models suitable for clinical use while reducing training time. The first network repairs low-resolution defective models, while the second network enhances the volumetric resolution of the repaired model. We have tested our method in simulations and real-life surgical practices, producing implants that fit naturally and precisely match defect boundaries, particularly for skull defects above the Frankfort horizontal plane.
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Affiliation(s)
- Chieh-Tsai Wu
- Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | | | - Yau-Zen Chang
- Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Mechanical Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
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3
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Li J, Ellis DG, Kodym O, Rauschenbach L, Rieß C, Sure U, Wrede KH, Alvarez CM, Wodzinski M, Daniol M, Hemmerling D, Mahdi H, Clement A, Kim E, Fishman Z, Whyne CM, Mainprize JG, Hardisty MR, Pathak S, Sindhura C, Gorthi RKSS, Kiran DV, Gorthi S, Yang B, Fang K, Li X, Kroviakov A, Yu L, Jin Y, Pepe A, Gsaxner C, Herout A, Alves V, Španěl M, Aizenberg MR, Kleesiek J, Egger J. Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge. Med Image Anal 2023; 88:102865. [PMID: 37331241 DOI: 10.1016/j.media.2023.102865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 06/20/2023]
Abstract
Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.
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Affiliation(s)
- Jianning Li
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria.
| | - David G Ellis
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Oldřich Kodym
- Graph@FIT, Brno University of Technology, Brno, Czech Republic
| | - Laurèl Rauschenbach
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Christoph Rieß
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Ulrich Sure
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Karsten H Wrede
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Carlos M Alvarez
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Marek Wodzinski
- AGH University of Science and Technology, Department of Measurement and Electronics, Krakow, Poland; University of Applied Sciences Western Switzerland (HES-SO Valais), Information Systems Institute, Sierre, Switzerland
| | - Mateusz Daniol
- AGH University of Science and Technology, Department of Measurement and Electronics, Krakow, Poland
| | - Daria Hemmerling
- AGH University of Science and Technology, Department of Measurement and Electronics, Krakow, Poland
| | - Hamza Mahdi
- Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Evan Kim
- Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Cari M Whyne
- Sunnybrook Research Institute, Toronto, ON, Canada; Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, M5T 1P5, Canada
| | - James G Mainprize
- Sunnybrook Research Institute, Toronto, ON, Canada; Calavera Surgical Design Inc., Toronto, ON, Canada
| | - Michael R Hardisty
- Sunnybrook Research Institute, Toronto, ON, Canada; Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, M5T 1P5, Canada
| | - Shashwat Pathak
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | - Chitimireddy Sindhura
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | | | - Degala Venkata Kiran
- Department of Mechanical Engineering, Indian Institute of Technology, Tirupati, India
| | - Subrahmanyam Gorthi
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | - Bokai Yang
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Ke Fang
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Xingyu Li
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Artem Kroviakov
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
| | - Lei Yu
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
| | - Yuan Jin
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Adam Herout
- Graph@FIT, Brno University of Technology, Brno, Czech Republic
| | - Victor Alves
- ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
| | | | - Michele R Aizenberg
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Jan Egger
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria.
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Ma W, Yang X, Wang Q, Huang K, Huang X. Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion. Cells 2022; 11. [PMID: 36552872 DOI: 10.3390/cells11244107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/03/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
3D point clouds are gradually becoming more widely used in the medical field, however, they are rarely used for 3D representation of intracranial vessels and aneurysms due to the time-consuming data reconstruction. In this paper, we simulate the incomplete intracranial vessels (including aneurysms) in the actual collection from different angles, then propose Multi-Scope Feature Extraction Network (MSENet) for Intracranial Aneurysm 3D Point Cloud Completion. MSENet adopts a multi-scope feature extraction encoder to extract the global features from the incomplete point cloud. This encoder utilizes different scopes to fuse the neighborhood information for each point fully. Then a folding-based decoder is applied to obtain the complete 3D shape. To enable the decoder to intuitively match the original geometric structure, we engage the original points coordinates input to perform residual linking. Finally, we merge and sample the complete but coarse point cloud from the decoder to obtain the final refined complete 3D point cloud shape. We conduct extensive experiments on both 3D intracranial aneurysm datasets and general 3D vision PCN datasets. The results demonstrate the effectiveness of the proposed method on three evaluation metrics compared to baseline: our model increases the F-score to 0.379 (+21.1%)/0.320 (+7.7%), reduces Chamfer Distance score to 0.998 (-33.8%)/0.974 (-6.4%), and reduces the Earth Mover's Distance to 2.750 (17.8%)/2.858 (-0.8%).
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Wodzinski M, Daniol M, Socha M, Hemmerling D, Stanuch M, Skalski A. Deep learning-based framework for automatic cranial defect reconstruction and implant modeling. Comput Methods Programs Biomed 2022; 226:107173. [PMID: 36257198 DOI: 10.1016/j.cmpb.2022.107173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/19/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE This article presents a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. METHODS We propose a two-step deep learning-based method using a modified U-Net architecture to perform the defect reconstruction, and a dedicated iterative procedure to improve the implant geometry, followed by an automatic generation of models ready for 3-D printing. We propose a cross-case augmentation based on imperfect image registration combining cases from different datasets. Additional ablation studies compare different augmentation strategies and other state-of-the-art methods. RESULTS We evaluate the method on three datasets introduced during the AutoImplant 2021 challenge, organized jointly with the MICCAI conference. We perform the quantitative evaluation using the Dice and boundary Dice coefficients, and the Hausdorff distance. The Dice coefficient, boundary Dice coefficient, and the 95th percentile of Hausdorff distance averaged across all test sets, are 0.91, 0.94, and 1.53 mm respectively. We perform an additional qualitative evaluation by 3-D printing and visualization in mixed reality to confirm the implant's usefulness. CONCLUSION The article proposes a complete pipeline that enables one to create the cranial implant model ready for 3-D printing. The described method is a greatly extended version of the method that scored 1st place in all AutoImplant 2021 challenge tasks. We freely release the source code, which together with the open datasets, makes the results fully reproducible. The automatic reconstruction of cranial defects may enable manufacturing personalized implants in a significantly shorter time, possibly allowing one to perform the 3-D printing process directly during a given intervention. Moreover, we show the usability of the defect reconstruction in a mixed reality that may further reduce the surgery time.
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Affiliation(s)
- Marek Wodzinski
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland; MedApp S.A., Krakow, Poland; Information Systems Institute, University of Applied Sciences Western Switzerland, Sierre, Switzerland.
| | - Mateusz Daniol
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland; MedApp S.A., Krakow, Poland
| | - Miroslaw Socha
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland
| | - Daria Hemmerling
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland
| | - Maciej Stanuch
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland; MedApp S.A., Krakow, Poland
| | - Andrzej Skalski
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland; MedApp S.A., Krakow, Poland
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Egger J, Wild D, Weber M, Bedoya CAR, Karner F, Prutsch A, Schmied M, Dionysio C, Krobath D, Jin Y, Gsaxner C, Li J, Pepe A. Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform. J Digit Imaging 2022; 35:340-355. [PMID: 35064372 PMCID: PMC8782222 DOI: 10.1007/s10278-021-00574-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 02/06/2023] Open
Abstract
Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. In this context, we introduce Studierfenster (www.studierfenster.at): a free, non-commercial open science client-server framework for (bio-)medical image analysis. Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. When participants were asked about their overall impression of Studierfenster in an ISO standard (ISO-Norm) questionnaire, a mean of 6.3 out of 7.0 possible points were achieved. The evaluation also provided insights into the results achievable with Studierfenster in practice, by comparing these with two ground truth segmentations performed by a physician of the Medical University of Graz in Austria. In this contribution, we presented an online environment for (bio-)medical image analysis. In doing so, we established a client-server-based architecture, which is able to process medical data, especially 3D volumes. Our online environment is not limited to medical applications for humans. Rather, its underlying concept could be interesting for researchers from other fields, in applying the already existing functionalities or future additional implementations of further image processing applications. An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448–456, 68], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. Furthermore, applications in entirely non-medical research in which images/volumes need to be processed are also thinkable, such as those in optical measuring techniques, astronomy, or archaeology.
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Affiliation(s)
- Jan Egger
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia.
- Computer Algorithms for Medicine Laboratory, Graz, Austria.
- Institute for Artificial Intelligence in Medicine, AI-guided Therapies, University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.
| | - Daniel Wild
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Maximilian Weber
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christopher A Ramirez Bedoya
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Florian Karner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Alexander Prutsch
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Michael Schmied
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christina Dionysio
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Dominik Krobath
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Yuan Jin
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Research Center for Connected Healthcare Big Data, ZhejiangLab, 311121, Hangzhou, Zhejiang, China
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Jianning Li
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
- Institute for Artificial Intelligence in Medicine, AI-guided Therapies, University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia
- Computer Algorithms for Medicine Laboratory, Graz, Austria
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Li J, Krall M, Trummer F, Memon AR, Pepe A, Gsaxner C, Jin Y, Chen X, Deutschmann H, Zefferer U, Schäfer U, Campe GV, Egger J. MUG500+: Database of 500 high-resolution healthy human skulls and 29 craniotomy skulls and implants. Data Brief 2021; 39:107524. [PMID: 34815988 PMCID: PMC8591340 DOI: 10.1016/j.dib.2021.107524] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/25/2021] [Indexed: 11/19/2022] Open
Abstract
In this article, we present a skull database containing 500 healthy skulls segmented from high-resolution head computed-tomography (CT) scans and 29 defective skulls segmented from craniotomy head CTs. Each healthy skull contains the complete anatomical structures of human skulls, including the cranial bones, facial bones and other subtle structures. For each craniotomy skull, a part of the cranial bone is missing, leaving a defect on the skull. The defects have various sizes, shapes and positions, depending on the specific pathological conditions of each patient. Along with each craniotomy skull, a cranial implant, which is designed manually by an expert and can fit with the defect, is provided. Considering the large volume of the healthy skull collection, the dataset can be used to study the geometry/shape variabilities of human skulls and create a robust statistical model of the shape of human skulls, which can be used for various tasks such as cranial implant design. The craniotomy collection can serve as an evaluation set for automatic cranial implant design algorithms.
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Affiliation(s)
- Jianning Li
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
- Corresponding authors.
| | - Marcell Krall
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Florian Trummer
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Afaque Rafique Memon
- Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Antonio Pepe
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
| | - Christina Gsaxner
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Yuan Jin
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Research Center for Connected Healthcare Big Data, ZhejiangLab, Hangzhou, Zhejiang, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Ulrike Zefferer
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Ute Schäfer
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Gord von Campe
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Jan Egger
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
- Corresponding authors.
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8
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Kodym O, Španěl M, Herout A. Deep learning for cranioplasty in clinical practice: Going from synthetic to real patient data. Comput Biol Med 2021; 137:104766. [PMID: 34425418 DOI: 10.1016/j.compbiomed.2021.104766] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 08/10/2021] [Indexed: 11/17/2022]
Abstract
Correct virtual reconstruction of a defective skull is a prerequisite for successful cranioplasty and its automatization has the potential for accelerating and standardizing the clinical workflow. This work provides a deep learning-based method for the reconstruction of a skull shape and cranial implant design on clinical data of patients indicated for cranioplasty. The method is based on a cascade of multi-branch volumetric CNNs that enables simultaneous training on two different types of cranioplasty ground-truth data: the skull patch, which represents the exact shape of the missing part of the original skull, and which can be easily created artificially from healthy skulls, and expert-designed cranial implant shapes that are much harder to acquire. The proposed method reaches an average surface distance of the reconstructed skull patches of 0.67 mm on a clinical test set of 75 defective skulls. It also achieves a 12% reduction of a newly proposed defect border Gaussian curvature error metric, compared to a baseline model trained on synthetic data only. Additionally, it produces directly 3D printable cranial implant shapes with a Dice coefficient 0.88 and a surface error of 0.65 mm. The outputs of the proposed skull reconstruction method reach good quality and can be considered for use in semi- or fully automatic clinical cranial implant design workflows.
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Affiliation(s)
- Oldřich Kodym
- Department of Computer Graphics and Multimedia, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic.
| | - Michal Španěl
- Department of Computer Graphics and Multimedia, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic
| | - Adam Herout
- Department of Computer Graphics and Multimedia, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic
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