CT-based segmentation of thoracic vertebrae using deep learning and computation of the kyphotic angle

Nadeem, Syed Ahmed; Comellas, A.P.; Guha, Indranil; Regan, Elizabeth A.; Hoffman, Eric A.; Saha, Punam K.

doi:10.1117/12.2613065

Cited by 2 publications

(5 citation statements)

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“…Experimental results (Nadeem et al, 2021) have demonstrated that the conventional freeze‐and‐grow algorithm outperforms DL‐based results (Charbonnier et al, 2017; Jin et al, 2017). It was observed the conventional freeze‐and‐grow method coupled with DL significantly improved the computational efficiency and allows airway detection at low radiation CT scans (Nadeem et al, 2021; Nadeem, Comellas, Guha, et al, 2022). Different steps of a DL‐powered freeze‐and‐grow algorithm are illustrated in Figure 7.…”

Section: Deep Learning For Assessment Of Pulmonary and Related Anatomiesmentioning

confidence: 99%

“…Buerger et al (2020) applied multi‐staged U‐nets to first achieve coarse binary classification and then, subsequently, accomplish multi‐class classification of individual vertebrae at a finer scale. Recently, Nadeem, Comellas, Guha, et al (2022) adopted a new approach of using DL as a low‐level tool to obtain a voxel‐level vertebral likelihood map and then applied multi‐parametric iterative connectivity and centerline analysis to segment and identify individual vertebrae (see Figure 9). Lu et al (2018) applied U‐net on MRI to segment the spinal column and a multi‐class CNN to assess stenosis grading.…”

Section: Deep Learning For Assessment Of Pulmonary and Related Anatomiesmentioning

confidence: 99%

“…Intermediate results of CT‐based automated vertebral segmentation, labeling, and fracture assessment on a sagittal image slice using deep learning (DL) and center‐line based morphologic analysis of anterior, middle, and posterior vertebral heights (Nadeem, Comellas, Guha, et al, 2022). (a) CT representation of the spinal column on a truncated sagittal image slice.…”

Section: Deep Learning For Assessment Of Pulmonary and Related Anatomiesmentioning

confidence: 99%

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A survey on artificial intelligence in pulmonary imaging

Saha

Nadeem

Comellas

2023

WIREs Data Min & Knowl

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Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID‐19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This article presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL‐based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Computational Intelligence Application Areas > Science and Technology

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Section: Deep Learning For Assessment Of Pulmonary and Related Anatomiesmentioning

confidence: 99%

Section: Deep Learning For Assessment Of Pulmonary and Related Anatomiesmentioning

confidence: 99%

Section: Deep Learning For Assessment Of Pulmonary and Related Anatomiesmentioning

confidence: 99%

See 1 more Smart Citation

A survey on artificial intelligence in pulmonary imaging

Saha

Nadeem

Comellas

2023

WIREs Data Min & Knowl

Self Cite

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“…A preliminary version of the DL-based freeze-and-grow (FG) method for segmentation and labelling of individual vertebrae, referred to in item (1), was presented in a conference paper. 33 The DL network, presented in the current paper, was retrained using a larger multi-site training dataset. More importantly, the intensity autocorrelation method, referred to in item (2), for separating apparently fused vertebrae in CT presented here was not presented previously.…”

Section: Introductionmentioning

confidence: 99%

“…for trained human experts; (4) optimization of the decision threshold parameter for fracture detection using Receiver‐Operating‐Characteristic (ROC) curve analysis; (5) large‐scale evaluation on chest CT data from a nationwide multi‐site pulmonary research study 13 ; and (6) assessment of generalizability of the method to low dose CT imaging. A preliminary version of the DL‐based freeze‐and‐grow (FG) method for segmentation and labelling of individual vertebrae, referred to in item (1), was presented in a conference paper 33 . The DL network, presented in the current paper, was retrained using a larger multi‐site training dataset.…”

Section: Introductionmentioning

confidence: 99%

Chest CT‐based automated vertebral fracture assessment using artificial intelligence and morphologic features

Nadeem,

Comellas,

Regan

et al. 2024

Medical Physics

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BackgroundSpinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.PurposeWe present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi‐parametric freeze‐and‐grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.MethodsA chest CT‐based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel‐level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi‐parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior‐posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi‐site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL‐based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.ResultsThe vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image‐level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.ConclusionsOur CT‐based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population‐based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population‐based studies has increased to reduce cumulative radiation exposure.

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CT-based segmentation of thoracic vertebrae using deep learning and computation of the kyphotic angle

Cited by 2 publications

References 23 publications

A survey on artificial intelligence in pulmonary imaging

A survey on artificial intelligence in pulmonary imaging

Chest CT‐based automated vertebral fracture assessment using artificial intelligence and morphologic features

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