Stochastic PCA-Based Bone Models from Inverse Transform Sampling: Proof of Concept for Mandibles and Proximal Femurs

Pascoletti, Giulia; Aldieri, Alessandra; Terzini, Mara; Bhattacharya, Pinaki; Calı̀, Michele; Zanetti, Elisabetta M.

doi:10.3390/app11115204

Cited by 15 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An emerging approach to mitigate this problem is the combination of computational musculoskeletal modeling with statical shape analysis ( van Houcke et al, 2020a ; Pascoletti et al, 2021 ). Aiming to bypass the aforementioned restrictions, Audenaert and colleagues developed a validated pipeline for semi-automated shape model-based segmentation of the lower limb based on computed tomography (CT) imaging ( van Haver et al, 2014a ; Audenaert et al, 2019a ).…”

Section: Introductionmentioning

confidence: 99%

Personalized statistical modeling of soft tissue structures in the knee

Oevelen

Duquesne²,

Peiffer³

et al. 2023

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented.Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment.Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14–0.48), 0.35 mm (range 0.16–0.53), 0.39 mm (range 0.15–0.80) and 0.75 mm (range 0.16–1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99–1.59), 0.91 mm (0.75–1.33), 2.93 mm (range 1.85–4.66) and 2.04 mm (1.88–3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus.Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.

show abstract

Section: Introductionmentioning

confidence: 99%

Personalized statistical modeling of soft tissue structures in the knee

Oevelen

Duquesne²,

Peiffer³

et al. 2023

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…We believe this unique clinically validated dataset would pave the way for future population studies in the field. More specifically, data augmentation techniques using machine learning [69,70] can be applied to the Open-Full-Jaw dataset to expand its size and variability by generating plausible synthetic data. In addition, this would enable us to use deep learning methods, which require a large amount of data for training.…”

Section: Discussionmentioning

confidence: 99%

Open-Full-Jaw: An open-access dataset and pipeline for finite element models of human jaw

Gholamalizadeh,

Moshfeghifar,

Ferguson

et al. 2022

Preprint

View full text Add to dashboard Cite

Background. State-of-the-art finite element studies on human jaws are mostly limited to the geometry of a single patient. In general, developing accurate patient-specific computational models of the human jaw acquired from cone-beam computed tomography (CBCT) scans is labor-intensive and non-trivial, which involves time-consuming human-in-theloop procedures, such as segmentation, geometry reconstruction, and re-meshing tasks. Therefore, with the current practice, researchers need to spend considerable time and effort to produce finite element models (FEMs) to get to the point where they can use the models to answer clinically-interesting questions. Besides, any manual task involved in the process makes it difficult for the researchers to reproduce identical models generated in the literature. Hence, a quantitative comparison is not attainable due to the lack of surface/volumetric meshes and FEMs. Methods.We share an open-access repository composed of 17 patient-specific computational models of human jaws and the utilized pipeline for generating them for reproducibility of our work. The used pipeline minimizes the required time for processing and any potential biases in the model generation process caused by human intervention. It gets the segmented geometries with irregular and dense surface meshes and provides reduced, adaptive, watertight, and conformal surface/volumetric meshes, which can directly be used in finite element (FE) analysis.Results. We have quantified the variability of our 17 models and assessed the accuracy of the developed models from three different aspects; (1) the maximum deviations from the input meshes using the Hausdorff distance as an error measurement, (2) the quality of the developed volumetric meshes, and (3) the stability of the FE models under two different scenarios of tipping and biting.Conclusions. The obtained results indicate that the developed computational models are precise, and they consist of quality meshes suitable for various FE scenarios. We believe the provided dataset of models including a high geometrical variation obtained from 17 different models will pave the way for population studies focusing on the biomechanical behavior of human jaws.

show abstract

“…In the field of orthopaedics, SSMs have been employed for many applications regarding the femur, since it is one of the most implanted districts within the skeleton ( Lindner, et al, 2013 ; Sarkalkan et al, 2014 ; Noussios, et al, 2019 ). More in detail, SSMs have been employed to automatize the segmentation of the femur from clinical images ( Bryan et al, 2010 ); to predict missing parts from portions of the distal femur ( Ramme, et al, 2011 ) or to predict more complex femoral shapes from incomplete or sparse data obtained through less invasive methods (e.g., DXA images) ( Humbert et al, 2017 ); to create new virtual instances ( Pascoletti, et al, 2021 ; La Mattina, et al, 2023 ); to classify subjects and identify diseases ( Waarsing, et al, 2010 ; Aldieri, et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal

Betti,

Aldieri,

Cristofolini

2024

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

Statistical Shape Models (SSMs) are widely used in orthopedics to extract the main shape features from bone regions (e.g., femur). This study aims to develop an SSM of the femoral medullary canal, investigate its anatomical variability, and assess variations depending on canal length. The canals were isolated from 72 CT femur scans, through a threshold-based segmentation. A region of interest (ROI) was selected; sixteen segments were extracted from the ROI, ranging from 25% of the full length down to the most distal segment. An SSM was developed to identify the main modes of variation for each segment. The number of Principal Components (PCs) needed to explain at least 90% of the shape variance were three/four based on the length of the canal segment. The study examined the relationship between the identified PCs and geometric parameters like length, radius of curvature, ellipticity, mean diameter, and conicity, reporting range and percentage variation of these parameters for each segment. The SSMs provide insights into the anatomical variability of the femoral canal, emphasizing the importance of considering different segments to capture shape variations at various canal length. These findings can contribute for the design of personalized orthopedic implants involving the distal femur.

show abstract

Stochastic PCA-Based Bone Models from Inverse Transform Sampling: Proof of Concept for Mandibles and Proximal Femurs

Cited by 15 publications

References 32 publications

Personalized statistical modeling of soft tissue structures in the knee

Personalized statistical modeling of soft tissue structures in the knee

Open-Full-Jaw: An open-access dataset and pipeline for finite element models of human jaw

A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal

Contact Info

Product

Resources

About