Statistical Shape and Appearance Models in Osteoporosis

Castro-Mateos, Isaac; Pozo, José M.; Cootes, Timothy F.; Wilkinson, JM; Eastell, Richard; Frangi, Alejandro F.

doi:10.1007/s11914-014-0206-3

Cited by 22 publications

(9 citation statements)

References 63 publications

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“…To investigate inhalation therapeutics in disease-modified lungs, statistical shape modeling (SSM) has been explored in developing population-average shape and generating new "simulated" shape models [80]. This method was first developed as an imaging processing algorithm in computer graphics in the 1990s and has become popular in other disciplines like biomechanics [81,82], biometrics [83], evolutional biology [84], anthropology [85], and forensics [86]. SSM uses the dataset comparing multiple shape models as a matrix and extracts the predominant features as leading eigenvectors, which will be further used in a linear combination to create new shape models [80].…”

Section: Synergize Ct-based Lung Generation and Algorithm-based Remodelingmentioning

confidence: 99%

Lung Physiological Variations in COVID-19 Patients and Inhalation Therapy Development for Remodeled Lungs

Walfield

April

et al. 2021

SciMed. J.

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In response to the unmet need for effective treatments for symptomatic patients, research efforts of inhaled therapy for COVID-19 patients have been pursued since the pandemic began. However, inhalation drug delivery to the lungs is sensitive to the lung anatomy and physiology, which can be significantly altered due to the viral infection. The ensued ventilation heterogeneity will change distribution and thus dosimetry of inhaled medications, rendering previous correlations concepts? of pulmonary drug delivery in healthy lungs less reliable. In this study, we first reviewed the recent developments of inhaled therapeutics and vaccines, as well as the latest knowledge of the lung structural variations documented by CT of COVID-19 patients' lungs. We then quantified the volume ratios of the poorly aerated lungs and non-aerated lungs in eight COVID-19 patients, which ranged 2-8% and 0.5-3%, respectively. The need to consider the diseased lung physiologies in estimating pulmonary delivery was emphasized. Diseased lung geometries with varying lesion sites and complexities were reconstructed using Statistical Shape Modeling (SSM). A new segmentation method was applied that could generate patient-specific lung geometries with an increased number of branching generations. The synergy of the CT-based lung segmentation and SSM-based airway variation showed promise for developing representative COVID-infected lung morphological models and investigating inhalation therapeutics in COVID-19 patients. Doi: 10.28991/SciMedJ-2021-0303-1 Full Text: PDF

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Section: Synergize Ct-based Lung Generation and Algorithm-based Remodelingmentioning

confidence: 99%

Lung Physiological Variations in COVID-19 Patients and Inhalation Therapy Development for Remodeled Lungs

Walfield

April

et al. 2021

SciMed. J.

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“…SSM has been used in recent studies to successfully detect healthy morphological changes associated with aging, functional activity, osteoporosis and osteoarthritis [15][16][17][18]. However, this is the first time that SSM has been used to explore associations between intrinsic spine shape variations and LDD.…”

Section: Discussionmentioning

confidence: 99%

Is intrinsic lumbar spine shape associated with lumbar disc degeneration? An exploratory study

Deane

Pavlova

Lim

et al. 2020

BMC Musculoskelet Disord

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Background: Lumbar disc degeneration (LDD) is a condition associated with recurrent low back pain (LBP). Knowledge regarding effective management is limited. As a step towards the identification of risk, prognostic or potentially modifiable factors in LDD patients, the aim of this study was to explore the hypothesis that intrinsic lumbar spine shape is associated with LDD and clinical outcomes in symptomatic adults. Methods: 3 T MRI was used to acquire T2-weighted sagittal images (L1-S1) from 70 healthy controls and LDD patients (mean age 49 years, SD 11, range 31-71 years). Statistical Shape Modelling (SSM) was used to describe lumbar spine shape. SSM identified variations in lumbar shape as 'modes' of variation and quantified deviation from the mean. Intrinsic shape differences were determined between LDD groups using analysis of variance with posthoc comparisons. The relationship between intrinsic shape and self-reported function, mental health and quality of life were also examined. Results: The first 7 modes of variation explained 91% of variance in lumbar shape. Higher LDD sum scores correlated with a larger lumbar lordosis (Mode 1 (55% variance), P = 0.02), even lumbar curve distribution (Mode 2 (12% variance), P = 0.05), larger anterior-posterior (A-P) vertebral diameter (Mode 3 (10% variance), P = 0.007) and smaller L4-S1 disc spaces (Mode 7 (2% variance), P ≤ 0.001). In the presence of recurrent LBP, LDD was associated with a larger A-P vertebral diameter (Mode 3) and a more even lumbar curvature with smaller L5/S1 disc spaces (Mode 4), which was significantly associated with patient quality of life (P = 0.002-0.04, r p = 0.43-0.61)). Conclusions: This exploratory study provides new evidence that intrinsic shape phenotypes are associated with LDD and quality of life in patients. Longitudinal studies are required to establish the potential role of these risk or prognostic shape phenotypes.

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“…On the one side, there is a bottom-up, datadriven direction which we like to refer to as "imagebased modelling" or more broadly, "phenomenological modelling". Perhaps starting with the success of statistical shape modelling (Young and Frangi, 2009;Castro-Mateos et al, 2014), and successive developments leading to computational atlasing, computational anatomy (Miller et al, 2015) and disease state fingerprinting (Kumar et al, 2012;Mattila et al, 2011), these and other developments accelerated by machine learning emphasize learning and inference of knowledge directly from vast amounts of imaging data (Kansagra et al, 2016;Medrano-Gracia et al, 2015;Margolies et al, 2016). This confluence of image-based computational modelling with developments on population imaging (Volzke et al, 2012) will increasingly underpin computational models and phenotypes of health and disease.…”

Section: The Trend: From Data To Wisdom and Backmentioning

confidence: 99%

Precision Imaging: more descriptive, predictive and integrative imaging

Frangi

Taylor

Gooya

2016

Medical Image Analysis

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Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological modelbased imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.

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Statistical Shape and Appearance Models in Osteoporosis

Cited by 22 publications

References 63 publications

Lung Physiological Variations in COVID-19 Patients and Inhalation Therapy Development for Remodeled Lungs

Lung Physiological Variations in COVID-19 Patients and Inhalation Therapy Development for Remodeled Lungs

Is intrinsic lumbar spine shape associated with lumbar disc degeneration? An exploratory study

Precision Imaging: more descriptive, predictive and integrative imaging

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