Landmark-free morphometric analysis of knee osteoarthritis using joint statistical models of bone shape and articular space variability

Charon, Nicolas; Islam, Asef; Zbijewski, Wojciech

doi:10.1117/1.jmi.8.4.044001

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…With high‐dimensional classification tasks such as this, machine learning algorithms may prove useful in learning a decision rule to differentiate between positive and negative examples in the data. Support vector machines (SVMs) are a popular supervised learning method that in the healthcare domain alone have been used in predicting ailments such as diabetes, cancer, neurodegenerative diseases such as Alzheimer's and orthopedic conditions such as osteoarthritis (Battineni et al, 2019; Charon et al, 2021; Razzaghi et al, 2016; Yu et al, 2010). They have recently been used specifically for diagnosis of lung diseases such as Chronic Obstructive Pulmonnary Disease (COPD), lung nodules and lung cancer (Shafi et al, 2022; Sui et al, 2015; Xia et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Can lung airway geometry be used to predict autism? A preliminary machine learning‐based study

Islam,

Ronco,

Becker

et al. 2023

The Anatomical Record

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The goal of this study is to assess the feasibility of airway geometry as a biomarker for autism spectrum disorder (ASD). Chest computed tomography images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. Fifty‐four scans were obtained for analysis, including 31 ASD cases and 23 controls. A feature selection and classification procedure using principal component analysis and support vector machine achieved a peak cross validation accuracy of nearly 89% using a feature set of eight airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branching angles between children with ASD and the control population.

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Section: Methodsmentioning

confidence: 99%

Can lung airway geometry be used to predict autism? A preliminary machine learning‐based study

Islam,

Ronco,

Becker

et al. 2023

The Anatomical Record

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“…Furthermore, we compare to an LDDMM-based approach [12], where the emerging latent shape space is estimated by a PCA on the learned initial momentum vectors of training shapes. This is motivated by [27,6], who used a PCA on momenta for disease classification. For comparability, we use the same template and control points as in our method.…”

Section: Methodsmentioning

confidence: 99%

Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations

Lüdke

Amiranashvili

Ambellan

et al. 2022

Lecture Notes in Computer Science

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Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-ofthe-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available 5 .

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“…2,3 Further details of the landmark-free atlas estimation framework can be found in Ref. 4. The implementation was based on the FshapesTk MATLAB library 5 and ran using its CUDA subroutines to accelerate computations.…”

Section: Atlas Estimationmentioning

confidence: 99%

Body mass classification from skeletal elements using landmark-free morphological atlas estimation with diffeomorphic shape mapping

Shi

Meckel

et al. 2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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: We investigate whether femur morphology is affected by body mass (BM). To establish deformations associated with obesity, we propose an atlas estimation framework based on diffeomorphic shape mapping that relaxes the point correspondence requirement common to many conventional shape modeling approaches. : The study sample consisted of femora from 18 normal weight (BMI between 20-25) and 18 obese (BMI > 30) individuals (Texas State University Donated Skeletal Collection). Bone surface models (2,500 vertices and approximately 5,000 faces) were generated from CT scans of the specimens (512x512 matrix, 0.625x0.625x0.5 mm voxels). The surface models were input to an optimization algorithm that yielded an atlas representation of shape variability consisting of a mean bone template and diffeomorphic deformations matching the template onto each specimen. The accuracy of normal weight vs. obese classification using principal atlas deformation modes was established in leave-one-out experiments with Support Vector Machine (SVM) classifier. : We achieved 75% classification accuracy in leave-one-out SVM experiments, indicating the possibility of functional skeletal adaptations to increased body mass. By visualizing the bone surface deformation given by the SVM classification direction, we found that morphological alterations associated with obesity might include relative thickening of the femoral neck and the trochanters, and retroversion of the femoral head. : The landmark-free atlas estimation algorithm enabled detection of morphological femur variants that might be predictive of elevated body mass.

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Landmark-free morphometric analysis of knee osteoarthritis using joint statistical models of bone shape and articular space variability

Cited by 6 publications

References 30 publications

Can lung airway geometry be used to predict autism? A preliminary machine learning‐based study

Can lung airway geometry be used to predict autism? A preliminary machine learning‐based study

Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations

Body mass classification from skeletal elements using landmark-free morphological atlas estimation with diffeomorphic shape mapping

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