scholarly journals Shadow Peak: Accurate Real-time Bone Segmentation for Ultrasound and Developmental Dysplasia of the Hip

10.29007/w172 ◽  
2019 ◽  
Author(s):  
Prashant Pandey ◽  
Niamul Quader ◽  
Kishore Mulpuri ◽  
Pierre Guy ◽  
Rafeef Garbi ◽  
...  

Confidence-weighted structured phase symmetry (CSPS) is a state-of-the-art bone seg- mentation technique for ultrasound (US), which has been recently proposed for automatic diagnosis for developmental dysplasia of the hip (DDH). However, CSPS relies on complex image phase feature analysis which is computationally expensive, and in our preliminary tests we have found it to be sometimes inaccurate. We evaluate a simpler alternative segmentation technique which we previously published, called Shadow Peak (SP), which uses intensity analysis to perform fast and accurate US bone segmentation. On average, SP segmentation ran 15 times faster for 2D US images, when tested on 15 hip images of pediatric patients. Furthermore, SP improves the segmentation F-score to 94%, compared to 72% when using CSPS segmentation.

10.29007/zh41 ◽  
2018 ◽  
Author(s):  
Olivia Paserin ◽  
Niamul Quader ◽  
Kishore Mulpuri ◽  
Anthony Cooper ◽  
Emily Schaeffer ◽  
...  

Although physical and ultrasound (US)-based screening for congenital deformities of the hip (developmental dysplasia of the hip, or DDH) is routinely performed in most countries, one of the most commonly performed maneuvers done under ultrasound observation - dynamic assessment - has been shown to be relatively unreliable and is associated with significant misdiagnosis rates, on the order of 29%.Our overall research objective is to develop a quantitative method of assessing hip instability, which we hope will standardize diagnosis across different raters and health-centers, and may perhaps improve reliability of diagnosis. To quantify dynamic assessment, we propose to use the variability in femoral head coverage (FHC) measurements within multiple US scans collected during a dynamic assessment. In every US scan, we use our recently-developed automatic FHC measuring tool which leverages phase symmetry features to approximate vertical cortex of ilium and a random forest classifier to identify approximate location of the femoral head. Having estimated FHC in each scan, we estimate the change in FHC across all the US scans during a dynamic assessment and compare this change with variability of FHC found in previous studies.Our findings - in a dynamic assessment on an infant done by an orthopaedic surgeon, the femoral centre moved by up to 19% of its diameter during distraction, from 55% FHC to 74% FHC. This change in FHC is slightly greater than its variability in static US scans reported in previous studies, suggesting that the distraction force likely produced a real lateral displacement. Our clinician’s qualitative assessment concluded the hip to be normal as this degree of distraction was not indicative of instability. This suggests that our technique likely has sufficient resolution and repeatability to quantify differences in laxity between stable and unstable hips, although this presumption will have to be confirmed in a subsequent study with additional subjects. The long-term significance of this approach to evaluating dynamic assessments may lie in increasing early diagnostic sensitivity in order to prevent dysplasia remaining undetected prior to manifesting itself in early adulthood joint disease.


10.29007/bncb ◽  
2018 ◽  
Author(s):  
Mateo Villa ◽  
Guillaume Dardenne ◽  
Maged Nasan ◽  
Hoel Letissier ◽  
Chafiaa Hamitouche ◽  
...  

In CAOS, ultrasound imaging has been proposed as a solution for obtaining the specific bone morphology of the patient, avoiding limitations of existing technologies. However, this imaging modality presents different drawbacks that make difficult the automatic bone segmentation. A new algorithm, based on Fully Convolutional Networks (FCN), is proposed. The aim of this paper is to compare and validate this method with (1) a manual segmentation that was performed by three independent experts, and (2) a state of the art method called Confidence in Phase Symmetry (CPS). The FCN based approach outperforms the CPS algorithm and the RMSE is close to the manual segmentation variability.


2017 ◽  
Vol 46 (1) ◽  
pp. 54-61 ◽  
Author(s):  
Nabil Alassaf

Objective Closed reduction (CR) is a noninvasive treatment for developmental dysplasia of the hip (DDH), and this treatment is confirmed intraoperatively. This study aimed to develop a preoperative estimation model of the probability of requiring open reduction (OR) for DDH. Methods The study design was cross-sectional by screening all patients younger than 2 years who had attempted CR between October 2012 and July 2016 by a single surgeon. Potential diagnostic determinants were sex, age, side, bilaterality, International Hip Dysplasia Institute (IHDI) grade, and acetabular index (AI). An intraoperative arthrogram was the reference standard. A logistic regression equation was built from a reduced model. Bootstrapping was performed for internal validity. Results A total of 164 hips in 104 patients who met the inclusion criteria were analysed. The prevalence of CR was 72.2%. Independent factors for OR were older age, higher IHDI grade, and lower AI. The probability of OR = 1/[1 + exp − (−2.753 + 0.112 × age (months) + 1.965 × IHDI grade III (0 or 1) + 3.515 × IHDI grade IV (0 or 1) − 0.058 × AI (degrees)]. The area under the curve was 0.79. Conclusion This equation is an objective tool that can be used to estimate the requirement for OR.


Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1174
Author(s):  
Si-Wook Lee ◽  
Hee-Uk Ye ◽  
Kyung-Jae Lee ◽  
Woo-Young Jang ◽  
Jong-Ha Lee ◽  
...  

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.


2021 ◽  
Author(s):  
Hans‐Christen Husum ◽  
Arash Gaffari ◽  
Laura Amalie Rytoft ◽  
Jens Svendsson ◽  
Søren Harving ◽  
...  

2021 ◽  
Vol 29 ◽  
pp. S10
Author(s):  
T.D. Capellini ◽  
P. Muthuirulan ◽  
Z. Liu ◽  
A.M. Kiapour ◽  
J. Sieker ◽  
...  

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