Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features

Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A.; Massey, S.; Gunka, V.; Rohling, Robert

doi:10.1007/s11548-015-1202-5

Cited by 23 publications

(21 citation statements)

References 27 publications

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“…Two recent studies, namely Pesteie et al ( 2015 ) and Hetherington et al ( 2017 ), exploited artificial neural networks trained with ultrasound images to automatically detect the optimal vertebra level and injection plane for percutaneous spinal needle injections. They used different ML techniques on the same type of medical images.…”

Section: Literature Review: Resultsmentioning

confidence: 99%

Machine Learning in Orthopedics: A Literature Review

Cabitza

Locoro

Banfi

2018

Front. Bioeng. Biotechnol.

190

125

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In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance.

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Section: Literature Review: Resultsmentioning

confidence: 99%

Machine Learning in Orthopedics: A Literature Review

Cabitza

Locoro

Banfi

2018

Front. Bioeng. Biotechnol.

190

125

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show abstract

“…Previously, we developed a system for automatic ultrasound plane classification for spinal injections namely, epidural and facet joint injections. 23 The system was trained on 1090 planes of 3D ultrasound volumes which were collected from 13 volunteers. Annotations of an expert sonographer was used for training to learn the signatures of the anatomical landmarks of the target planes.…”

Section: Resultsmentioning

confidence: 99%

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

et al. 2017

Self Cite

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Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

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“…The extracted features should be robust to different changes like translation, rotation, and scaling. Thus, initially, the segments are processed using two levels of DWT [35], and then multiple directional Walsh Hadamard 2D transform [36] is applied to each segment in the low‐frequency sub‐band for extracting multi‐directional features. DWT is applied to extract some features with diverse scales from the image by including successive low‐pass and high‐pass filters.…”

Section: Proposed Methodology For Identifying Brain Tumour Gradesmentioning

confidence: 99%

Performance enhancement of image segmentation analysis for multi‐grade tumour classification in MRI image

Kandan¹,

Murugeswari²

2020

IET image process

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Medical applications have a massive footprint in human's day‐to‐day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substantial, but it is a time‐consuming process. Both the normal and abnormal tissues found in the brain look similar, which increases the difficulty of the tumour detection process. The digital image needs to be processed to obtain an exact tumour detection result. The tumour detection process comprises five different stages, such as pre‐processing, segmentation, feature extraction, feature selection, and classification. In this proposed work, hybrid wavelet Hadamard transform and grey‐level co‐occurrence matrix are included for feature extraction. Feature selection utilises sequential forward selection, which is an easy greedy search algorithm. This algorithm chooses only the predominant features for classification. The classification uses a hybrid support vector machine and adaptive emperor penguin optimisation. The experimental analysis shows the efficiency of the proposed work in terms of accuracy, specificity, and sensitivity values by computing the true positive, false positive, true negative, and false negative.

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Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features

Cited by 23 publications

References 27 publications

Machine Learning in Orthopedics: A Literature Review

Machine Learning in Orthopedics: A Literature Review

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

Performance enhancement of image segmentation analysis for multi‐grade tumour classification in MRI image

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