Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data

Jiménez-Pastor, Ana; B, Fos-Guarinos; García-Castro, Fabio; Garcia-Juan, David; Glocker, Ben; Martí‐Bonmatí, Luis

doi:10.1007/s11547-019-01079-9

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…In breast cancer research, the use of radiomics combined with multiple imaging modalities, clinical information and machine learning methods are under investigation, not only to detect malignant lesions and discriminating tumor grade, but also for identifying prognostic factors; for instance, the response to neoadjuvant chemotherapy (NAC) as well as the risk of tumor recurrence [66], similar to other settings [67][68][69][70][71].…”

Section: Texture Analysis and Prognosis-focus On Breast Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

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Section: Texture Analysis and Prognosis-focus On Breast Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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“…Jimenez-Pastor et al introduced a 2-stage decision forests and morphological image processing technique to automatically detect and identify vertebral bodies from arbitrary field-of-view body CT scans. 25 Vertebrae automatic detection network achieved satisfying accuracy and precision on CT or unannotated MRI. 26 , 27 An automated framework could detect myelopathic areas, combining diffusion tensor imaging (DTI) metrics with SVM.…”

Section: Resultsmentioning

confidence: 94%

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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“…However, recent research has been exploring the use of deep learning-based AI systems which are able to perform multiple tasks at the basic and advanced level in a single model [1]. Vertebrae are by far the most investigated structure, with AI systems reaching > 90% DICE and > 90% accuracy in the majority of studies included in our review, both using DIP [28][29][30][31][32][33][34][35][36][37][38][39][40][41]43,44,[48][49][50]53,61,62] and deep learning models [67,69,77,[80][81][82][83][84]. In particular, a study from Lee et al [40] proposed a model to obtain an automated segmentation of lumbar pedicles from CT images in order to increase accuracy and safety during transpedicular screw placement.…”

Section: Discussionmentioning

confidence: 99%

“…With regards to the papers that performed both localization and identification, Jimenez-Pastor et al [39] used a Decision Forest and morphological image processing to localize and identify vertebrae on 272 CT images, achieving a localization error of 13.7 mm and an accuracy of 74.8%. Lee et al [40] exploited threshold and thinning-based integrated cost on CT images of 19 subjects, for the localization and identification of lumbar pedicles in order to increase accuracy and safety during transpedicular screw placement, with a localization error of 0.14 mm and 93.2% accuracy.…”

Section: Digital Image Processingmentioning

confidence: 99%

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review

D’Antoni

Russo

Ambrosio

et al. 2021

IJERPH

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Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Feature Extraction”, “Segmentation”, “Computer Vision”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Low Back Pain”, “Lumbar”. Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen–Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems’ autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

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Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data

Cited by 12 publications

References 18 publications

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Current Applications of Machine Learning in Spine: From Clinical View

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review

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