3-D Ultrasound Imaging Reliability of Measuring Dysplasia Metrics in Infants.

Developmental dysplasia of the hip is a hip abnormality that ranges from mild acetabular dysplasia to irreducible femoral head dislocations. While 2-D B-mode ultrasound (US)-based dysplasia metrics or disease metrics are currently used clinically to diagnose developmental dysplasia of the hip, such estimates suffer from high inter-exam variability. In this work, we propose and evaluate 3-D US-derived dysplasia metrics that are automatically computed and demonstrate that these automatically derived dysplasia metrics are considerably more reproducible. The key features of our automatic method are (i) a random forest-based learning technique to remove regions across the coronal axis that do not contain bone structures necessary for dysplasia-metric extraction, thereby reducing outliers; (ii) a bone segmentation method that uses rotation-invariant and intensity-invariant filters, thus remaining robust to signal dropout and varying bone morphology; (iii) a novel slice-based learning and 3-D reconstruction strategy to estimate a probability map of the hypoechoic femoral head in the US volume; and (iv) formulae for calculating the 3-D US-derived dysplasia metrics. We validate our proposed method on real clinical data acquired from 40 infant hip examinations. Results show a considerable (around 70%) reduction in variability in two key 3-D US-derived dysplasia metrics compared with their 2-D counterparts.

Ultrasound Bone Segmentation: A Scoping Review of Techniques and Validation Practices.

Ultrasound bone segmentation is an important yet challenging task for many clinical applications. Several works have emerged attempting to improve and automate bone segmentation, which has led to a variety of computational techniques, validation practices and applied clinical scenarios. We characterize this exciting and growing body of research by reviewing published ultrasound bone segmentation techniques. We review 56 articles in detail and categorize and discuss the image analysis techniques that have been used for bone segmentation. We highlight the general trends of this field in terms of clinical motivation, image analysis techniques, ultrasound modalities and the types of validation practices used to quantify segmentation performance. Finally, we present an outlook on promising areas of research based on the unaddressed needs for solving ultrasound bone segmentation.

A Systematic Review and Meta-analysis on the Reproducibility of Ultrasound-based Metrics for Assessing Developmental Dysplasia of the Hip.

BACKGROUND: The purposes of this study were to (1) perform a systematic review of articles that reported agreement or reproducibility in repeated diagnosis of developmental dysplasia of the hip (DDH) using ultrasound imaging, (2) estimate the reproducibility in the available dysplasia metrics, and (3) compare reproducibility of the available dysplasia metrics. METHODS: A systematic review of the Medline and Embase databases was performed by using a search strategy formulated from our research question: "For infants at risk of DDH, are US imaging-based diagnoses reproducible?" Two reviewers independently identified articles for inclusion in the systematic review, and then assessed the quality of the included studies using the Guidelines for Reporting Reliability and Agreement Studies guideline. Variability and agreement-related statistics in the included studies were extracted and included in a meta-analysis for summarizing the available statistics. The reproducibility of the available dysplasia metrics was compared, with a Bonferroni correction made to adjust for multiple comparisons. RESULTS: Twenty eight studies were included in the systematic review. Overall, the quality of the included studies was moderate (average, 10.7/15; range, 6 to 12). Graf's alpha angle had the lowest interexamination variability of the metrics assessed, followed by Graf's beta angle (the variability of the alpha angle was 10% lower than the variability of the beta angle, P<0.05). However, despite Graf's angles having lower variability compared with other dysplasia metrics, their actual variability was still problematically high. This finding was supported by the low intraclass correlation and Kappa coefficient values reported in the included studies. There was also evidence to suggest that the reproducibility in DDH diagnosis has potentially worsened over time. CONCLUSIONS: Overall, we found high variability and low agreement in all reported dysplasia metrics. Furthermore, in the last 3 decades, the repeatability of dysplasia metrics has not markedly improved and may even have declined, indicating a genuine need for improving repeatability and reliability of ultrasound-based DDH diagnosis. LEVEL OF EVIDENCE: Level III-systematic review of level III studies.

Automatic Evaluation of Scan Adequacy and Dysplasia Metrics in 2-D Ultrasound Images of the Neonatal Hip.

Ultrasound (US) imaging of an infant's hip joint is widely used for early detection of developmental dysplasia of the hip. In current US-based diagnosis of developmental dysplasia of the hip, trained clinicians acquire US images and, if they judge them to be adequate (i.e., to contain relevant hip joint structures), analyze them manually to extract clinically useful dysplasia metrics. However, both the scan adequacy classification and dysplasia metrics extraction steps exhibit significant variability within and between both clinicians and institutions, which can result in significant over- and undertreatment rates. To reduce the subjectivity resulting from this variability, we propose a computational image analysis technique that automatically identifies adequate images and subsequently extracts dysplasia metrics from these 2-D US images. Our automatic method uses local phase symmetry-based image measures to robustly identify intensity-invariant geometric features of bone/cartilage boundaries from the US images. Using the extracted geometric features, we trained a random forest classifier to classify images as adequate or inadequate, and in the adequate images we used a subset of the geometric features to calculate key dysplasia metrics. We validated our method on a data set of 693 US scans collected from 35 infants. Our approach produces excellent agreement with clinician adequacy classifications (area under the receiver operating characteristic curve = 0.985) and in reducing variability in the measured developmental dysplasia of the hip metrics (p < 0.05). The automatically computed dysplasia metrics appear to be slightly biased toward higher Graf categories than the manually estimated metrics, which could potentially reduce missed early diagnoses.