Image Processing-based Method for Automatic Design of Patient-Specific Cranial Implant for Additive Manufacturing

Venugopal, Vysakh; Ghalsasi, Omkar; McConaha, Matthew; Xu, Alice; Forbes, Jonathan A.; Anand, Sam

doi:10.1016/j.promfg.2021.06.090

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…For example, Prasoon et al exhibited how knee cartilage can be segmented from scans automatically by adopting the technology 9 . Moreover, authors have outlined how the design of custom medical devices, including cranial implants 6 , 10 and ocular prostheses 11 , can be achieved automatically from CT data via similar techniques. Burge et al used various machine learning techniques to develop a ‘fully automated mass-customisation pipeline’ for producing TKR components from bi-planar X-ray images 12 .…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

Burge

Jeffers

Myant

2023

Sci Rep

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The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with ‘ground truth’ 3D models for each test subject’s bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants.

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Section: Introductionmentioning

confidence: 99%

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

Burge

Jeffers

Myant

2023

Sci Rep

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Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge

Ellis

Kodym

et al. 2023

Medical Image Analysis

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Computer Simulation-Based Multi-Objective Optimisation of Additively Manufactured Cranial Implants

Moya,

Rivas,

Quiza

et al. 2024

Technologies

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Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality, but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.

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Image Processing-based Method for Automatic Design of Patient-Specific Cranial Implant for Additive Manufacturing

Cited by 3 publications

References 29 publications

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge

Computer Simulation-Based Multi-Objective Optimisation of Additively Manufactured Cranial Implants

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