Ultrasound imaging and segmentation of bone surfaces: A review

Hacihaliloglu, Ilker

doi:10.1142/s2339547817300049

Cited by 52 publications

(24 citation statements)

References 76 publications

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“…Thanks to their simplicity, thresholding methods have been applied to segment a variety of MSK structures, such as bone, bone marrow lesions, knee effusion, or muscles. [21][22][23][24][25] In particular, local or global thresholding techniques are often applied to radiography or computed tomography (CT) images to segment mineralized structures that present high contrast with surrounding structures. [26][27][28][29] One limitation of threshold-based segmentation is that threshold values depend on the modality, scanner, or acquisition parameters.…”

Section: Thresholdingmentioning

confidence: 99%

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Cuadra

Favre

Omoumi

2020

Semin Musculoskelet Radiol

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Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.

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Section: Thresholdingmentioning

confidence: 99%

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Cuadra

Favre

Omoumi

2020

Semin Musculoskelet Radiol

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“…In order to provide a radiation-free, real-time, cost effective imaging alternative, for intra-operative fluoroscopy, special attention has been given to incorporate ultrasound (US) into computer assisted orthopedic surgery (CAOS) procedures [1]. However, problems such as high levels of noise, imaging artifacts, limited field of view and bone boundaries appearing several millimeters (mm) in thickness have hindered the wide spread adaptability of US-guided CAOS systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, problems such as high levels of noise, imaging artifacts, limited field of view and bone boundaries appearing several millimeters (mm) in thickness have hindered the wide spread adaptability of US-guided CAOS systems. This has resulted in the development of automated bone segmentation and enhancement methods [1]. Accurate and robust segmentation is important for improved guidance in US-based CAOS procedures.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN

Wang

Patel

Hacihaliloglu

2018

Lecture Notes in Computer Science

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Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a multi-feature guided convolutional neural network (CNN) architecture is proposed for simultaneous enhancement, segmentation, and classification of bone surfaces from US data. The proposed CNN consists of two main parts: a pre-enhancing net, that takes the concatenation of B-mode US scan and three filtered image features for the enhancement of bone surfaces, and a modified U-net with a classification layer. The proposed method was validated on 650 in vivo US scans collected using two US machines, by scanning knee, femur, distal radius and tibia bones. Validation, against expert annotation, achieved statistically significant improvements in segmentation of bone surfaces compared to state-of-the-art.

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“…Due to its real-time, 2D/3D, and non-radiation-based imaging capabilities, ultrasound (US) has been investigated as an alternative imaging modality in various CAOS procedures (Hacihaliloglu, 2017a). Despite promising results, US has not become the standard-of-care in CAOS.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Guided Pedicle Screw Entry Point Identification for Spinal Fusion Surgery

Qu¹,

Vives²,

Hacihaliloglu³

EPiC Series in Health Sciences

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Accurate identification of the location the vertebra and corresponding pedicle is critical during pedicle screw insertion for percutaneous spinal fusion surgery. Currently, two dimensional (2D) fluoroscopy based navigation systems have extensive usage in spinal fusion surgery. Relying on 2D projection images for screw guidance results in high misplacement rates. Furthermore, fluoroscopy-based guidance exposes the surgical staff and patient to harmful ionizing radiation. Real-time non-radiation-based ultrasound (US) is a potential alternative to intra-operative fluoroscopy. However, accurate interpretation of noisy US data and manual operation of the transducer during data collection remains a challenge. In this work we investigate the potential of using multi-modal deep convolutional neural network (CNN) architectures for fully automatic identification of vertebra level and pedicle from US data. Our proposed network achieves 93.54% vertebra identification accuracy on in vivo US data collected from 27 subjects.

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Ultrasound imaging and segmentation of bone surfaces: A review

Cited by 52 publications

References 76 publications

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN

Ultrasound Guided Pedicle Screw Entry Point Identification for Spinal Fusion Surgery

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