Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation

Neubert, Aleš; Fripp, Jürgen; Engstrom, Craig; Schwarz, Dániel; Weber, Marc‐André; Crozier, Stuart

doi:10.1016/j.compmedimag.2015.05.002

Cited by 4 publications

(4 citation statements)

References 60 publications

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“…Also, it is robust to outliers in the input space and can rank the importance of variables considered in classification. Another interesting aspect of statistical shape modelling is reconstruction [54][55][56]. The advantage of applying SPHARM to the shape reconstruction problem is their low complexity.…”

Section: Discussionmentioning

confidence: 99%

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Statistical shape models of cuboid, navicular and talus bones

Melińska¹,

Romaszkiewicz²,

Wagel³

et al. 2017

J Foot Ankle Res

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BackgroundThe aim was to develop statistical shape models of the main human tarsal bones that would result in novel representations of cuboid, navicular and talus.MethodsFifteen right and 15 left retrospectively collected computed tomography data sets from male individuals, aged from 17 to 63 years, with no known foot pathology were collected. Data were gathered from 30 different subjects. A process of model building includes image segmentation, unifying feature position, mathematical shape description and obtaining statistical shape geometry.ResultsOrthogonal decomposition of bone shapes utilising spherical harmonics was employed providing means for unique parametric representation of each bone. Cross-validated classification results based on parametric spherical harmonics representation showed high sensitivity and high specificity greater than 0.98 for all considered bones.ConclusionsThe statistical shape models of cuboid, navicular and talus created in this work correspond to anatomically accurate atlases that have not been previously considered. The study indicates high clinical potential of statistical shape modelling in the characterisation of tarsal bones. Those novel models can be applied in medical image analysis, orthopaedics and biomechanics in order to provide support for preoperative planning, better diagnosis or implant design.Electronic supplementary materialThe online version of this article (doi:10.1186/s13047-016-0178-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…Another interesting aspect of statistical shape modelling is reconstruction [54–56]. The advantage of applying SPHARM to the shape reconstruction problem is their low complexity.…”

Section: Discussionmentioning

confidence: 99%

Statistical shape models of cuboid, navicular and talus bones

Melińska¹,

Romaszkiewicz²,

Wagel³

et al. 2017

J Foot Ankle Res

View full text Add to dashboard Cite

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“…This is known as a binary classification. For example, the performance value of one IVD classification system was 86.5%, and this was based on a sparse shape reconstruction from a statistical shape model [ 32 ]. Additionally, an accuracy of 92.78% was reported by a study that classified normal disks and disk bulge by using a program called IVD Descriptor [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Pan¹,

Zhang²,

He³

et al. 2021

JMIR Med Inform

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Background Disk herniation and disk bulge are two common disorders of lumbar intervertebral disks (IVDs) that often result in numbness, pain in the lower limbs, and lower back pain. Magnetic resonance (MR) imaging is one of the most efficient techniques for detecting lumbar diseases and is widely used for making clinical diagnoses at hospitals. However, there is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists. Objective The aim of this study was to present an automatic system for diagnosing disk bulge and herniation that saves time and can effectively and significantly reduce the workload of radiologists. Methods The diagnosis of lumbar vertebral disorders is highly dependent on medical images. Therefore, we chose the two most common diseases—disk bulge and herniation—as research subjects. This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. Results This system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100% accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100%. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7%, 84.4%, 92.1%, 90.4%, and 84.2%, respectively. Conclusions The automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis.

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“…In particular, artificial neural networks have proven particularly effective in medical image processing and have been used in a wide range of applications from automatically determine body composition (Hemke et al 2020), cartilage pathologies (Liu et al 2018) and geometries (Nikolopoulos et al 2020), bone geometries (Ambellan et al 2019), and muscle volumes (Yeung et al 2019) and geometries (Ni et al 2019). Using a dataset of reconstructed anatomical structures or organs exists, statistical shape models have been used to extract features from anatomical data, using principal component analysis (Rodriguez-Florez et al 2017;Varzi et al 2015;Williams et al 2010) to create representations of anatomical tissue with associated principal components for bone (Grant et al 2020;Suwarganda et al 2019;Zhang and Besier 2017;Zhang et al 2014), cartilage (albeit indirectly) (Van Dijck et al 2018), meniscus (Dube et al 2018;Vrancken et al 2014), and other connective tissues (Neubert et al 2015). Once a statistical shape model has been created using a large sample of tissue morphometries (i.e., big data), weighted principal components can be used to reconstruct morphometry of a novel tissue using minimal (i.e., sparse) data.…”

Section: About Here>mentioning

confidence: 99%

Machine learning methods to support personalized neuromusculoskeletal modelling

Saxby

Killen

Pizzolato

et al. 2020

Biomech Model Mechanobiol

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Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified and big data/machine learning approaches for implementation are presented together with recommendations for further research.

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Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation

Cited by 4 publications

References 60 publications

Statistical shape models of cuboid, navicular and talus bones

Statistical shape models of cuboid, navicular and talus bones

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Machine learning methods to support personalized neuromusculoskeletal modelling

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