Computer-based determination of the newborn’s femoral head coverage using three-dimensional ultrasound scans

Overhoff, Heinrich Martin; Heinze, Peter; Lazovic, Djordje; Jan, Ute von

doi:10.1007/bfb0056291

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The first attempt to automatically detect relevant landmarks from medical images of the infant pelvis could be traced back to 1997, when Overhoff et al [8], introduced an image processing-based approach to determine the region of interest (ROI) of the acetabulum and approximate the sphere of the femoral head in 3D ultrasound images. The findings of the study were further extended [9] to detect the acetabular landmarks used to assess the relevant angle measurements. More image processing-based algorithms such as the Hough transform were proposed [10] to offer a fully automated approach for the measurement of the acetabular cartilage in MRI medical images.…”

Section: Related Literaturementioning

confidence: 99%

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach

Jan,

Rahman,

Busaleh

et al. 2023

J. Imaging

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Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.

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Section: Related Literaturementioning

confidence: 99%

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach

Jan,

Rahman,

Busaleh

et al. 2023

J. Imaging

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“…Although its use is far from being widespread in clinical practice, the use of 3D ultrasound is rapidly growing and might provide certain advantages over 2D screening [7]. In [8], [9], [10], effort was made on the determination and visualization of the femoral head from 3D ultrasound images. In this paper, we present an automatic algorithm to segment the femoral head and the acetabulum by adjusting a sphere and a paraboloid.…”

Section: Introductionmentioning

confidence: 99%

Parametric 3D Hip Joint Segmentation for the Diagnosis of Developmental Dysplasia

Luis‐García

Alberola-López

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper, we present a segmentation method for the hip joint from 3D ultrasound data. The proposed approach starts from a well-known energy formulation of the segmentation problem, and employs the extended local structure tensor as image feature in order to incorporate gray level and texture information in a common framework. Using the Kullback-Leibler distance as an intrinsic dissimilarity measure, the energy minimization is performed by estimating the optimal parameters of the shpere and paraboloid that best approximate the femoral head and acetabulum, respectively. A 2D level set segmentation of the iliac bone and an easy user interaction step allow for the introduction of the necessary constraints to make the energy minimization feasible. Experimental results over several data volumes show this approach to be capable of successfully approximating the anatomy of the hip joint by simple geometrical surfaces.

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Parametric 3D Hip Joint Segmentation for the Diagnosis of Developmental Dysplasia

Luis‐García

López²

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

In this paper, we present a segmentation method for the hip joint from 3D ultrasound data. The proposed approach starts from a well-known energy formulation of the segmentation problem, and employs the extended local structure tensor as image feature in order to incorporate gray level and texture information in a common framework. Using the Kullback-Leibler distance as an intrinsic dissimilarity measure, the energy minimization is performed by estimating the optimal parameters of the sphere and paraboloid that best approximate the femoral head and acetabulum, respectively. A 2D level set segmentation of the iliac bone and an easy user interaction step allow for the introduction of the necessary constraints to make the energy minimization feasible. Experimental results over several data volumes show this approach to be capable of successfully approximating the anatomy of the hip joint by simple geometrical surfaces.

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Computer-based determination of the newborn’s femoral head coverage using three-dimensional ultrasound scans

Cited by 3 publications

References 7 publications

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach

Parametric 3D Hip Joint Segmentation for the Diagnosis of Developmental Dysplasia

Parametric 3D Hip Joint Segmentation for the Diagnosis of Developmental Dysplasia

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