Expertise Modeling for Automated Planning of Acetabular Cup in Total Hip Arthroplasty Using Combined Bone and Implant Statistical Atlases

Otomaru, Itaru; Kobayashi, Kazuto; Okada, Toshiyuki; Nakamoto, Masahiko; Kagiyama, Yoshiyuki; Takao, Masaki; Sugano, Nobuhiko; Tada, Yutaka; Sato, Yoshinobu

doi:10.1007/978-3-642-04268-3_66

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Firstly, it exploited the algorithmic framework presented by the same author's group (Cerveri et al 2011) to automatically extract the acetabular shape from the hemi-pelvic bone. This approach, which moves along the ongoing development of automatic methodologies for orthopaedic surgical planning (Otomaru et al 2009;Weaver et al 2009;Urban et al 2011), was demonstrated to reduce the effect of the operator variability during the manual 3D segmentation. Secondly, the study was carried out on a significant number of acetabular shapes (38) taken from CT data-sets.…”

Section: Discussionmentioning

confidence: 99%

Patient-specific acetabular shape modelling: comparison among sphere, ellipsoid and conchoid parameterisations

Cerveri

Manzotti

Baroni

2012

Computer Methods in Biomechanics and Biomedical Engineering

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The shape of the human acetabular cup was commonly represented as a hemisphere, but different geometries and patient-specific shapes have been recently proposed in the literature. Our aim was to test the limits of the sphericity assumption by comparing three different parameterisations, namely the sphere, the ellipsoid and the rotational conchoid. Models of hip surfaces, reconstructed from CT scans taken from Caucasian race cadavers and patients, were automatically processed to extract the acetabular surface. Two separate analyses were carried out on the overall acetabular shape, including both the acetabular fossa and the lunate surface (case A) and acetabular cup represented by the lunate surface only (case B). Nonlinear gradient-based and evolutionary computation approaches were implemented for the fitting process. Minor differences from the three idealised geometries were detected (median values of the fitting errors < 1 mm). Nonetheless, the sphere fitting was found to be statistically different from both the ellipsoid (p < 2.50e - 10) and the conchoid (p < 1.07e - 09), whereas no statistical difference was detected between the ellipsoid and the conchoid for case A. Significance of the difference between ellipsoid and sphere (p < 4.55e - 12) and between conchoid and sphere (p < 1.93e - 11) was found for case B as well. Interestingly, for case B statistical difference was detected between the ellipsoid and the conchoid. In conclusion, we synthesise that the morphology of the overall acetabular cup can be parameterised both with an ellipsoid shape and with a conchoid shape as well with superior quality than the simple sphere. Differently, if one considers just the lunate surface, better fitting results are expected when using the ellipsoid.

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Section: Discussionmentioning

confidence: 99%

Patient-specific acetabular shape modelling: comparison among sphere, ellipsoid and conchoid parameterisations

Cerveri

Manzotti

Baroni

2012

Computer Methods in Biomechanics and Biomedical Engineering

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“…A study showed that patients with iliopsoas impingement on the acetabular cup which might induce pain had anterior cup overhang of more than 12 mm measured in CT data [ 61 ]. The distance prior to penetration was considered in automated planning methods [ 62 , 63 ]. A limit of 1 mm was chosen in order to prevent penetration.…”

Section: Methodsmentioning

confidence: 99%

The Patient-Specific Combined Target Zone for Morpho-Functional Planning of Total Hip Arthroplasty

Habor

Fischer

Tokunaga³

et al. 2021

JPM

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Background Relevant criteria for total hip arthroplasty (THA) planning have been introduced in the literature which include the hip range of motion, bony coverage, anterior cup overhang, leg length discrepancy, edge loading risk, and wear. The optimal implant design and alignment depends on the patient’s anatomy and patient-specific functional parameters such as the pelvic tilt. The approaches proposed in literature often consider one or more criteria for THA planning. but to the best of our knowledge none of them follow an integrated approach including all criteria for the definition of a patient-specific combined target zone (PSCTZ). Questions/purposes (1) How can we calculate suitable THA implant and implantation parameters for a specific patient considering all relevant criteria? (2) Are the resulting target zones in the range of conventional safe zones? (3) Do patients who fulfil these combined criteria have a better outcome score? Methods A method is presented that calculates individual target zones based on the morphology, range of motion and load acting on the hip joint and merges them into the PSCTZ. In a retrospective analysis of 198 THA patients, it was calculated whether the patients were inside or outside the Lewinnek safe zone, Dorr combined anteversion range and PSCTZ. The postoperative Harris Hip Scores (HHS) between insiders and outsiders were compared. Results 11 patients were inside the PSCTZ. Patients inside and outside the PSCTZ showed no significant difference in the HHS. However, a significant higher HHS was observed for the insiders of two of the three sub-target zones incorporated in the PSCTZ. By combining the sub-target zones in the PSCTZ, all PSCTZ insiders except one had an HHS higher than 90. Conclusions The results might suggest that, for a prosthesis implanted in the PSCTZ a low outcome score of the patient is less likely than using the conventional safe zones by Lewinnek and Dorr. For future studies, a larger cohort of patients inside the PSCTZ is needed which can only be achieved if the cases are planned prospectively with the method introduced in this paper. Clinical Relevance The method presented in this paper could help the surgeon combining multiple different criteria during THA planning and find the suitable implant design and alignment for a specific patient.

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“…Few studies have been published on automated preoperative THA templating. In an early study, Otomaru et al proposed automated AC planning using 3-D CT data [60]. They used two statistical atlases, a combined pelvis and cup statistical shape model (PC-SSM), and a statistical map of bone thickness (SM-BT) to detect the pelvis surface from CT data.…”

Section: Related Researchmentioning

confidence: 99%

“…Arthroplasty can benefit from these efforts and progress in medical imaging, leading to fully automatic decision-making for prosthesis selection. However, our literature survey found few studies related to decision automation [50], [54], [60]. Most of the published studies have investigated hip joint segmentation or compared the accuracy and reliability of manual preoperative THA planning [24]- [59].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Computer Vision Techniques for Automated Total Hip Arthroplasty Planning on 2-D Radiographs

Kim

Yoon

2022

IEEE Access

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Preoperative planning is mandatory for successful total hip arthroplasty (THA). In planning, the operating surgeon should decide the best type and size of THA components for the patient. However, most digital templating software only simulates acetate templating by overlaying the shape of the prosthesis components on a radiograph; the selection and positioning of the prostheses are performed manually depending on the operator's experience. Determining the optimal type and size of THA components is a repetitive and time-consuming task for digital and acetate templating. This study proposes a novel approach to automatically select and position THA components that are most suitable for the patient's bone anatomy. The approach consists of two phases: segmenting a hip anteroposterior (AP) radiographic image into five predefined anatomical regions using a fully convolutional neural network, and estimating the optimal sizes and positions of THA components using deep learning and computer vision technology. The experiments demonstrated that the accuracy of acetabular and femoral component size prediction within one size error was 78.9% and 70.9%, respectively. Compared with meta-analysis results from previous studies, our results are close to human level. An automated digital templating prototype system was developed using our research results and tested in a clinical setting to evaluate field adaptability. These processes are introduced in this study.INDEX TERMS Artificial intelligence (AI), automated 2-D templating, hip joint segmentation, implant size estimation, prostheses recommendation, total hip arthroplasty (THA)

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Expertise Modeling for Automated Planning of Acetabular Cup in Total Hip Arthroplasty Using Combined Bone and Implant Statistical Atlases

Cited by 10 publications

References 9 publications

Patient-specific acetabular shape modelling: comparison among sphere, ellipsoid and conchoid parameterisations

Patient-specific acetabular shape modelling: comparison among sphere, ellipsoid and conchoid parameterisations

The Patient-Specific Combined Target Zone for Morpho-Functional Planning of Total Hip Arthroplasty

Deep Learning and Computer Vision Techniques for Automated Total Hip Arthroplasty Planning on 2-D Radiographs

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