Statistical Shape Modeling Approach to Predict Missing Scapular Bone

Salhi, Asma; Burdin, Valérie; Boutillon, Arnaud; Brochard, Sylvain; Mutsvangwa, Tinashe; Borotikar, Bhushan

doi:10.1007/s10439-019-02354-6

Cited by 35 publications

(25 citation statements)

References 56 publications

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“…The clinical scenario relates to the estimation of a pre-morbid shape (and pose) from a partial observation of the joint for shape (and pose) feature. With regard to studies that focus on solving similar problem, the prediction of pre-morbid shoulder bone shape using SSMs has had some success in the literature [41], [42], [43]. The main drawback of these studies is that of maintaining anatomical joint space, as the relative position between the scapula and humerus is not taken into account when using single-object shape models.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Multi-object Gaussian Process Models

Fouefack¹,

Borotikar²,

Douglas³

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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In model-based medical image analysis, three features of interest are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical property. Often, these are modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. However, analysing multiple objects in an image using multiple single object models may lead to large errors and uncertainties, especially around organ boundaries. A question that comes to mind is what kind of advantages can be gained from combining the three features of interest in the same statistical space for analysing images. This study presents a statistical modelling method for automatic learning of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). A DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variation. Our method is defined in a continuous domain with a principled way to represent shape, pose and intensity feature classes in a linear space, based on deformation fields. A deformation field-based metric is adapted in the method for modelling shape and intensity feature variation as well as for comparing rigid transformations (pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including marginalisation and regression. Furthermore, they allow for adding additional pose feature variability on top of those obtained from the image acquisition process; what we term as permutation modelling. For image analysis tasks using DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of features fully probabilistic. We validate the method using controlled synthetic data and we perform experiments on bone structures from CT images of the shoulder to illustrate the efficacy of the model for pose and shape feature prediction. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications in a multitude of scenarios including the management of musculoskeletal disorders, clinical decision making and image processing.

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Section: Discussionmentioning

confidence: 99%

Dynamic Multi-object Gaussian Process Models

Fouefack¹,

Borotikar²,

Douglas³

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, being linear representations, these models are mathematically convenient [2]. Literature reports that SSMs have been integrated into medical workflows [3] to help clinicians diagnose pathologies [4], [5], design implants [6], [7], reconstruct 3D anatomy from 2D radiographs [8] or plan patient-specific intervention [9], [10]. Consequently, SSMs of bony structures have been developed in the literature which include but are not limited to the femur [4], [5], [11], [12], humerus [2], [13], pelvis [6], scapula [2], [10], [14], tibia [5], [12], vertebrae [15], and wrist [8].…”

Section: Introductionmentioning

confidence: 99%

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning

Boutillon,

Salhi,

Burdin

et al. 2022

Preprint

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Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit oneto-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. Methods: The proposed anatomically parameterized SSM (ANAT SSM ) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical space). This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT SSM . We further impose orthogonality constraints to the anatomical parameterization (OC-ANAT SSM ) to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters within each (five for femoral and six for scapular bone). Results: Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. Conclusion: This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs. Significance: The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patientspecific pre-surgery planning or in-surgery assessment.

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“…The Statistical Shape Model (SSM) enables an efficient parameterized representation of the shape even when only a few shape instances are available [2,3]. It is not only useful for the compact modeling of the anatomical shape variation in the dataset but also to generate new shapes simi-lar to the original data and to reconstruct missing parts of shapes [4,5].…”

Section: Introductionmentioning

confidence: 99%

Bone Surface Reconstruction and Clinical Features Estimation from Sparse Landmarks and Statistical Shape Models: A feasibility study on the femur

Asvadi¹,

Dardenne²,

Troccaz³

et al. 2021

Preprint

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In this study, we investigated a method allowing the determination of the femur bone surface as well as its mechanical axis from some easy-to-identify bony landmarks. The reconstruction of the whole femur is therefore performed from these landmarks using a Statistical Shape Model (SSM). The aim of this research is therefore to assess the impact of the number, the position, and the accuracy of the landmarks for the reconstruction of the femur and the determination of its related mechanical axis, an important clinical parameter to consider for the lower limb analysis. Two statistical femur models were created from our in-house dataset and a publicly available dataset. Both were evaluated in terms of average point-to-point surface distance error and through the mechanical axis of the femur. Furthermore, the clinical impact of using landmarks on the skin in replacement of bony landmarks is investigated. The predicted proximal femurs from bony landmarks were more accurate compared to on-skin landmarks while both had less than 3.5 • degrees mechanical axis angle deviation error. The results regarding the non-invasive determination of the mechanical axis are very encouraging and could open very interesting clinical perspectives for the analysis of the lower limb either for orthopedics or functional rehabilitation.

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Statistical Shape Modeling Approach to Predict Missing Scapular Bone

Cited by 35 publications

References 56 publications

Dynamic Multi-object Gaussian Process Models

Dynamic Multi-object Gaussian Process Models

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning

Bone Surface Reconstruction and Clinical Features Estimation from Sparse Landmarks and Statistical Shape Models: A feasibility study on the femur

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