MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images

Kuang, Xihe; Wu, Honghan; Dokos, Socrates; Zhang, Teng

doi:10.1109/embc44109.2020.9175987

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…18 Overall, deep learning pipelines for segmenting vertebrae and intervertebral discs (IVDs) on MRI provided high accuracy and eliminated laborious manual labeling. 19,20 Paugam et al used supervised neural networks to perform single-class or multi-class segmenting the grey and white matter of spinal cord on MRI, which showed good results on small amounts of data. 21 In addition, convolutional neural networks (CNNs) model showed state-of-the-art performance for automated lesion segmentation after spinal cord injury (SCI).…”

Section: Image Processing and Diagnosismentioning

confidence: 99%

Current Applications of Machine Learning in Spine: From Clinical View

Ren

Xie

et al. 2021

Global Spine Journal

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Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

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Section: Image Processing and Diagnosismentioning

confidence: 99%

Current Applications of Machine Learning in Spine: From Clinical View

Ren

Xie

et al. 2021

Global Spine Journal

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“…In recent years, most instance segmentation methods for spine image segmentation are based on CNNs-only networks, and only a few Transformer-based networks are employed. For example, Kuang et al (2020) built an unsupervised segmentation network for spine image segmentation using the rule-based region of interest (ROI) detection, a voting mechanism accompanied by a CNN network. Sekuboyina et al (2018) proposed a dual branch fully convolutional network that take advantages of both low-resolution attention information on two-dimensional sagittal slices and high-resolution segmentation context on three-dimensional patches for effective segmentation of the vertebrae.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

Chen,

Qian,

et al. 2024

Preprint

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Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (discs and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we evaluated 15 existing DNN models for lumbar spine MR image segmentation. We introduced a new data augmentation technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. The 15 image segmentation models are evaluated on our private in-house dataset and the public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and 95% Hausdorff Distance. The SSMSpine dataset are available at https://github.com/jiasongchen/SSMSpine.

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“…Thus, current diagnoses and follow-up assessments require extensive clinical experience and expertise to interpret the alignment parameters manually and assess the patient physical appearance, making fast and accurate alignment analyses challenging. 4 AI has shown great promise in managing spine disease, including disease detection 5 , classification, 6,7 segmentation, 8 and progression prediction, 9 mainly based on medical images. Previous studies on automatic spine alignment analysis [10][11][12] could directly or indirectly regress CAs from radiographs of the major curve but could not compute heterogeneous curve patterns 13 or investigate the curve types.…”

Section: Introductionmentioning

confidence: 99%

“…AI has shown great promise in managing spine disease, including disease detection 5 , classification, 6 7 segmentation, 8 and progression prediction, 9 mainly based on medical images. Previous studies on automatic spine alignment analysis 10 , 11 , 12 could directly or indirectly regress CAs from radiographs of the major curve but could not compute heterogeneous curve patterns 13 or investigate the curve types.…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation

et al. 2022

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Background Assessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability.Methods From December 2019 to November 2020, 1,542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices.Findings SpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2¢78 and 5¢52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3¢18°and 6¢32°coronally and sagittally. All predicted alignments were strongly correlated with GT (p < 0¢001, R 2 > 0¢97), with minimal overall difference visualised via Bland-Altman plots. For curve detections, 95¢7% sensitivity and 88¢1% specificity was achieved, and for severity classification, 88¢6-90¢8% sensitivity was obtained. For sagittal abnormalities, greater than 85¢2-88¢9% specificity and sensitivity were achieved.Interpretation The auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies.

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MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images

Cited by 10 publications

References 11 publications

Current Applications of Machine Learning in Spine: From Clinical View

Current Applications of Machine Learning in Spine: From Clinical View

Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation

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