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Wodzinski M, Kwarciak K, Daniol M, Hemmerling D. Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models. Comput Biol Med 2024; 182:109129. [PMID: 39265478 DOI: 10.1016/j.compbiomed.2024.109129] [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: 06/18/2024] [Revised: 08/28/2024] [Accepted: 09/06/2024] [Indexed: 09/14/2024]
Abstract
Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. The results show that latent diffusion models combined with vector quantized variational autoencoder outperform other generative augmentation strategies. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects, without the need to acquire costly and time-consuming annotations. The findings of the work will lead to easier, faster, and less expensive modeling of personalized cranial implants. This is beneficial to numerous people suffering from cranial injuries. The work constitutes a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.
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Affiliation(s)
- Marek Wodzinski
- AGH University of Krakow, Department of Measurement and Electronics, Kraków, al. Mickiewicza 30, PL32059, Poland; University of Applied Sciences Western Switzerland (HES-SO Valais), Information Systems Institute, Sierre, Rue de Technopôle 3, 3960, Switzerland.
| | - Kamil Kwarciak
- AGH University of Krakow, Department of Measurement and Electronics, Kraków, al. Mickiewicza 30, PL32059, Poland
| | - Mateusz Daniol
- AGH University of Krakow, Department of Measurement and Electronics, Kraków, al. Mickiewicza 30, PL32059, Poland
| | - Daria Hemmerling
- AGH University of Krakow, Department of Measurement and Electronics, Kraków, al. Mickiewicza 30, PL32059, Poland
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2
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Wilke F, Matthews H, Herrick N, Dopkins N, Claes P, Walsh S. A novel approach to craniofacial analysis using automated 3D landmarking of the skull. Sci Rep 2024; 14:12381. [PMID: 38811771 PMCID: PMC11137148 DOI: 10.1038/s41598-024-63137-1] [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: 02/07/2024] [Accepted: 05/24/2024] [Indexed: 05/31/2024] Open
Abstract
Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5 mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (> 0.988), with automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.
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Affiliation(s)
- Franziska Wilke
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Harold Matthews
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Noah Herrick
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Nichole Dopkins
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Susan Walsh
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA.
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3
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Li J, Ellis DG, Pepe A, Gsaxner C, Aizenberg MR, Kleesiek J, Egger J. Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model. J Med Syst 2024; 48:55. [PMID: 38780820 PMCID: PMC11116219 DOI: 10.1007/s10916-024-02066-y] [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: 07/28/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024]
Abstract
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
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Affiliation(s)
- Jianning Li
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany.
| | - David G Ellis
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria
| | - Michele R Aizenberg
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany
| | - Jan Egger
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany.
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4
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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] [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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5
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Wilke F, Matthews H, Herrick N, Dopkins N, Claes P, Walsh S. Automated 3D Landmarking of the Skull: A Novel Approach for Craniofacial Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579642. [PMID: 38405968 PMCID: PMC10888852 DOI: 10.1101/2024.02.09.579642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising 9,999 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (>0.988), and automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.
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Affiliation(s)
- Franziska Wilke
- Department of Biology, Indiana University Indianapolis, Indianapolis, USA
| | - Harold Matthews
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Noah Herrick
- Department of Biology, Indiana University Indianapolis, Indianapolis, USA
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nichole Dopkins
- Department of Biology, Indiana University Indianapolis, Indianapolis, USA
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Susan Walsh
- Department of Biology, Indiana University Indianapolis, Indianapolis, USA
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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] [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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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] [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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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. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 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] [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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