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Segmentation of anatomical structures in ultrasound images is a challenging task due to existence of artifacts inherit to the modality such as speckle noise, attenuation, shadowing, uneven textures and blurred boundaries. This paper presents a novel attention-based predict-refine network, called ACU2E-Net, for segmentation of soft-tissue structures in ultrasound images. The network consists of two modules: a predict module, which is built upon our newly proposed attentive coordinate convolution; and a novel multi-head residual refinement module, which consists of three parallel residual refinement modules. The attentive coordinate convolution is designed to improve the segmentation accuracy by perceiving the shape and positional information of the target anatomy. The proposed multi-head residual refinement module reduces both segmentation biases and variances by integrating residual refinement and ensemble strategies. Moreover, it avoids multi-pass training and inference commonly seen in ensemble methods. To show the effectiveness of our method, we collect a comprehensive dataset of thyroid ultrasound scans from 12 different imaging centers, and evaluate our proposed network against state-of-the-art segmentation methods. Comparisons against state-of-the-art models demonstrate the competitive performance of our newly designed network on both the transverse and sagittal thyroid images. Ablation studies show that proposed modules improve the segmentation Dice score of the baseline model from 79.62% to 80.97% and 82.92% while reducing the variance from 6.12% to 4.67% and 3.21% in transverse and sagittal views, respectively.

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BACKGROUND: Ultrasound for developmental dysplasia of the hip (DDH) is challenging for nonexperts to perform and interpret. Recording "sweep" images allows more complete hip assessment, suitable for automation by artificial intelligence (AI), but reliability has not been established. We assessed agreement between readers of varying experience and a commercial AI algorithm, in DDH detection from infant hip ultrasound sweeps. METHODS: We selected a full spectrum of poor-to-excellent quality images and normal to severe dysplasia, in 240 hips (120 single 2-dimensional images, 120 sweeps). For 12 readers (radiologists, sonographers, clinicians and researchers; 3 were DDH subspecialists), and a ultrasound-FDA-cleared AI software package (Medo Hip), we calculated interobserver reliability for alpha angle measurements by intraclass correlation coefficient (ICC2,1) and for DDH classification by Randolph Kappa. RESULTS: Alpha angle reliability was high for AI versus subspecialists (ICC=0.87 for sweeps, 0.90 for single images). For DDH diagnosis from sweeps, agreement was high between subspecialists (kappa=0.72), and moderate for nonsubspecialists (0.54) and AI (0.47). Agreement was higher for single images (kappa=0.80, 0.66, 0.49). AI reliability deteriorated more than human readers for the poorest-quality images. The agreement of radiologists and clinicians with the accepted standard, while still high, was significantly poorer for sweeps than 2D images (P<0.05). CONCLUSIONS: In a challenging exercise representing the wide spectrum of image quality and reader experience seen in real-world hip ultrasound, agreement on DDH diagnosis from easily obtained sweeps was only slightly lower than from single images, likely because of the additional step of selecting the best image. AI performed similarly to a nonsubspecialist human reader but was more affected by low-quality images.

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AIMS: Early diagnosis of developmental dysplasia of the hip (DDH) using ultrasound (US) is safe, effective and inexpensive, but requires high-quality scans. The effect of scan quality on diagnostic accuracy is not well understood, especially as artificial intelligence (AI) begins to automate such diagnosis. In this paper, we developed a 10-point scoring system for reporting DDH US scan quality, evaluated its inter-rater agreement and examined its effect on automated assessment by an AI system-MEDO-Hip. METHODS: Scoring was based on iliac wing straightness and angulation; visibility of labrum, os ischium and femoral head; motion; and other artifacts. Four readers from novice to expert separately scored the quality of 107 scans with this 10-point scale and with holistic grading on a scale of 1-5. MEDO-Hip interpreted the same scans, providing a diagnostic category or identifying the scan as uninterpretable. RESULTS: Inter-rater agreement for the 10-point scale was significantly higher than holistic scoring ICC 0.68 vs 0.93, p < 0.05. Inter-rater agreement on the categorisation of individual features, by Cohen's kappa, was highest for os ischium (0.67 ± 0.06), femoral head (0.65 ± 0.07) and iliac wing (0.49 ± 0.12) indices, and lower for the presence of labrum (0.21 ± 0.19). MEDO-Hip interpreted all images of a quality > 7 and flagged 13/107 as uninterpretable. These were low-quality images (3 ± 1.2 vs. 7 ± 1.8 in others, p < 0.05), with poor visualization of the os ischium and noticeable motion. AI accuracy in cases with quality scores < = 7 was 57% vs. 89% on other cases, p < 0.01. CONCLUSION: This study validates that our scoring system reliably characterises scan quality, and identifies cases likely to be misinterpreted by AI. This could lead to more accurate use of AI in DDH diagnosis by flagging low-quality scans likely to provide poor diagnosis up front.

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There is a crucial need for quick testing and diagnosis of patients during the COVID-19 pandemic. Lung ultrasound is an imaging modality that is cost-effective, widely accessible, and can be used to diagnose acute respiratory distress syndrome in patients with COVID-19. It can be used to find important characteristics in the images, including A-lines, B-lines, consolidation, and pleural effusion, which all inform the clinician in monitoring and diagnosing the disease. With the use of portable ultrasound transducers, lung ultrasound images can be easily acquired, however, the images are often of poor quality. They often require an expert clinician interpretation, which may be time-consuming and is highly subjective. We propose a method for fast and reliable interpretation of lung ultrasound images by use of deep learning, based on the Kinetics-I3D network. Our learned model can classify an entire lung ultrasound scan obtained at point-of-care, without requiring the use of preprocessing or a frame-by-frame analysis. We compare our video classifier against ground truth classification annotations provided by a set of expert radiologists and clinicians, which include A-lines, B-lines, consolidation, and pleural effusion. Our classification method achieves an accuracy of 90% and an average precision score of 95% with the use of 5-fold cross-validation. The results indicate the potential use of automated analysis of portable lung ultrasound images to assist clinicians in screening and diagnosing patients.

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PURPOSE: Since it is fast, inexpensive and increasingly portable, ultrasound can be used for early detection of Developmental Dysplasia of the Hip (DDH) in infants at point-of-care. However, accurate interpretation\is highly dependent on scan quality. Poor-quality images lead to misdiagnosis, but inexperienced users may not even recognize the deficiencies in the images. Currently, users assess scan quality subjectively, based on image landmarks which are prone to human errors. Instead, we propose using Artificial Intelligence (AI) to automatically assess scan quality. METHODS: We trained separate Convolutional Neural Network (CNN) models to detect presence of each of four commonly used ultrasound landmarks in each hip image: straight horizontal iliac wing, labrum, os ischium and midportion of the femoral head. We used 100 3D ultrasound (3DUS) images for training and validated the technique on a set of 107 3DUS images also scored for landmarks by three non-expert readers and one expert radiologist. RESULTS: We got AI ≥ 85% accuracy for all four landmarks (ilium = 0.89, labrum = 0.94, os ischium = 0.85, femoral head = 0.98) as a binary classifier between adequate and inadequate scan quality. Our technique also showed excellent agreement with manual assessment in terms of Intraclass Correlation Coefficient (ICC) and Cohen's kappa coefficient (K) for ilium (ICC = 0.81, K = 0.56), os ischium (ICC = 0.89, K = 0.63) and femoral head (ICC = 0.83, K = 0.66), and moderate to good agreement for labrum (ICC = 0.65, K = 0.33). CONCLUSION: This new technique could ensure high scan quality and facilitate more widespread use of ultrasound in population screening of DDH.

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PURPOSE: Developmental dysplasia of the hip (DDH) diagnosis by two-dimensional ultrasound (2DUS) can have poor inter-rater reliability. 3D ultrasound (3DUS) may be more reliably performed, particularly by novice users. We compared intra- and inter-rater reliability between expert and novice operators performing 2DUS and 3DUS for DDH. MATERIALS AND METHODS: Infants with suspected DDH were assessed with 2DUS and 3DUS. Novice operators had 1.5 h of training and Experts had 5-15 years' experience. Images included two 2DUS static and two 3DUS sweep images per operator. Image quality was assessed by 5-point system (yes/no: full femoral head; full acetabular roof; horizontal iliac wing; os ischium; absent motion/artifact). 2DUS indices (alpha angle, coverage) were measured centrally by a blinded reader with 2 years DDH US experience. 3DUS was post-processed by semi-automated custom software generating acetabular surface models, indices and estimated probability of DDH. Gold-standard diagnosis of each hip as normal, borderline or dysplastic was based on radiologist review of expert 2DUS. RESULTS: Thirty infants, mean age 10.8 weeks were enrolled. Quality scores were 2.7±1.2 Novice versus 4.9±0.3 Expert for 2DUS (p = 0.04), and 4.2±1.0 Novice versus 4.9±0.3 Expert for 3DUS (p = 0.99). Inter-rater reliability was poor for 2DUS (ICC=0.10 for alpha angle, 0.04 for acetabular coverage) and moderate to high for 3DUS (ICC=0.73-0.83 for alpha angle, 0.55 for acetabular coverage). Intra-rater reliability and diagnostic accuracy was higher for 3DUS than 2DUS. CONCLUSION: Novice operators can perform 3DUS for DDH with reliability and accuracy approaching expert sonographers. Novices perform 2DUS with poor reliability and accuracy. KEY POINTS: • Novice/expert inter-rater reliability improved from poor with 2DUS to moderate/high with 3DUS. • Novice operators using 3DUS correctly classified 57/58 (98%) of infant hips. • DDH can be reliably assessed by novice operators using 3DUS.

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Developmental dysplasia of the hip (DDH) in infants under 6 months of age is typically treated by the Pavlik harness (PH). During successful PH treatment, a subluxed/dislocated hip is spontaneously reduced into the acetabulum, and DDH undergoes self-correction. PH treatment may fail due to avascular necrosis (AVN) of the femoral head. An improved understanding of mechanical factors accounting for the success/failure of PH treatment may arise from investigating articular cartilage contact pressure (CCP) within a hip during treatment. In this study, CCP in a cartilaginous infant hip was investigated through patient-specific finite element (FE) modeling. We simulated CCP of the hip equilibrated at 90 deg flexion at abduction angles of 40 deg, 60 deg, and 80 deg. We found that CCP was predominantly distributed on the anterior and posterior acetabulum, leaving the superior acetabulum (mainly superolateral) unloaded. From a mechanobiological perspective, hypothesizing that excessive pressure inhibits growth, our results qualitatively predicted increased obliquity and deepening of the acetabulum under such CCP distribution. This is the desired and observed therapeutic effect in successful PH treatment. The results also demonstrated increase in CCP as abduction increased. In particular, the simulation predicted large magnitude and concentrated CCP on the posterior wall of the acetabulum and the adjacent lateral femoral head at extreme abduction (80 deg). This CCP on lateral femoral head may reduce blood flow in femoral head vessels and contribute to AVN. Hence, this study provides insight into biomechanical factors potentially responsible for PH treatment success and complications.

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Purpose To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US). Materials and Methods In this retrospective multi-institutional study, 3D US was added to conventional two-dimensional (2D) US of 1728 infants (mean age, 67 days; age range, 3-238 days) evaluated for DDH from January 2013 to December 2016. Clinical diagnosis after more than 6 months follow-up was normal (n = 1347), borderline (Graf IIa, later normalizing spontaneously; n = 140) or dysplastic (Graf IIb or higher, n = 241). Custom software accessible through the institution's research portal automatically calculated indexes including 3D posterior and anterior alpha angle and osculating circle radius from hip surface models generated with less than 1 minute of user input. Logistic regression predicted clinical diagnosis (normal = 0, dysplastic = 1) from 3D indexes (ie, age and sex). Output represented probability of hip dysplasia from 0 to 1 (output: >0.9, dysplastic; 0.11-0.89, borderline; <0.1, normal). Software can be accessed through the research portal. Results Area under the receiver operating characteristic curve was equivalently high for 3D US indexes and 2D US alpha angle (0.996 vs 0.987). Three-dimensional US helped to correctly categorize 97.5% (235 of 241) dysplastic and 99.4% (1339 of 1347) normal hips. No dysplastic hips were categorized as normal. Correct diagnosis was provided at initial 3D US scan in 69.3% (97 of 140) of the studies diagnosed as borderline at initial 2D US scans. Conclusion Automatically calculated 3D indexes of acetabular shape performed equivalently to high-quality 2D US scans at tertiary medical centers to help diagnose DDH. Three-dimensional US reduced the number of borderline studies requiring follow-up imaging by over two-thirds.

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PURPOSE: Developmental dysplasia of the hip (DDH) is a congenital deformity which in severe cases leads to hip dislocation and in milder cases to premature osteoarthritis. Image-aided diagnosis of DDH is partly based on Graf classification which quantifies the acetabular shape seen at two-dimensional ultrasound (2DUS), which leads to high inter-scan variance. 3D ultrasound (3DUS) is a promising alternative for more reliable DDH diagnosis. However, manual quantification of acetabular shape from 3DUS is cumbersome. METHODS: Here, we (1) propose a semiautomated segmentation algorithm to delineate 3D acetabular surface models from 3DUS using graph search; (2) propose a fully automated method to classify acetabular shape based on a random forest (RF) classifier using features derived from 3D acetabular surface models; and (3) test diagnostic accuracy on a dataset of 79 3DUS infant hip recordings (36 normal, 16 borderline, 27 dysplastic based on orthopedic surgeon assessment) in 42 patients. For each 3DUS, we performed semiautomated segmentation to produce 3D acetabular surface models and then calculated geometric features including the automatic [Formula: see text]lpha (AA), acetabular contact angle (ACA), kurtosis (K), skewness (S) and convexity (C). Mean values of features obtained from surface models were used as inputs to train a RF classifier. RESULTS: Surface models were generated rapidly (user time 46.2 s) via semiautomated segmentation and visually closely correlated with actual acetabular contours (RMS error 1.39 ± 0.7 mm). A paired nonparametric u test on of feature values in each category showed statistically significant variation (p < 0.001) for AA, ACA and convexity. The RF classifier was 100 % specific and 97.2 % sensitive in classifying normal versus dysplastic hips and yielded true positive rates of 94.4, 62.5 and 89.9 % for normal, borderline and dysplastic hips. CONCLUSIONS: The proposed technique reduces the subjectivity of image-aided DDH diagnosis and could be useful in clinical practice.

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Current imaging diagnosis of developmental dysplasia of the hip (DDH) in infancy relies on 2-D ultrasound (US), which is highly operator-dependent. 3-D US offers more complete, and potentially more reliable, imaging of infant hip geometry. We sought to validate the fidelity of acetabular surface models obtained by 3-D US against those obtained concurrently by magnetic resonance imaging (MRI). 3-D US and MRI scans were performed on the same d in 20 infants with normal to severely dysplastic hips (mean age, 57 d; range 13-181 d). 3-D US was performed by two observers using a Philips VL13-5 probe. Coronal 3-D multi-echo data image combination (MEDIC) magnetic resonance (MR) images (1-mm slice thickness) were obtained, usually without sedation, in a 1.5 T Siemens unit. Acetabular surface models were generated for 40 hips from 3-D US and MRI using semi-automated tracing software, separately by three observers. For each hip, the 3-D US and MRI models were co-registered to overlap as closely as possible using Amira software, and the root mean square (RMS) distances between points on the models were computed. 3-D US scans took 3.2 s each. Inter-modality variability was visually minimal. Mean RMS distance between corresponding points on the acetabular surface at 3-D US and MRI was 0.4 ± 0.3 mm, with 95% confidence interval <1 mm. Mean RMS errors for inter-observer and intra-observer comparisons were significantly less for 3-D US than for MRI, while inter-scan and inter-modality comparisons showed no significant difference. Acetabular geometry was reproduced by 3-D US surface models within 1 mm of the corresponding 3-D MRI surface model, and the 3-D US models were more reliable. This validates the fidelity of 3-D US modeling and encourages future use of 3-D US in assessing infant acetabulum anatomy, which may be useful to detect and monitor treatment of hip dysplasia.

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Diagnosis and surgical management of Developmental Dysplasia of the Hip (DDH) relies on physical examination and 2D ultrasound scanning. Magnetic Resonance Imaging (MRI) can be used to complement existing techniques and could be advantageous in treatment planning due to its larger field of view. In this paper we propose a semi-automatic method to segment surface models of the acetabulum from MRI images. The method incorporates clinical knowledge in the form of intensity priors which are integrated into a Random Walker (RW) formulation. We use a modified RW framework which compensates for incomplete or blurred boundaries in the image by using information from neighboring slices in the sequence incorporated as node weights. We conducted a pilot study to evaluate the segmentation on a set of 10 infant hip MRI sequences using a 1.5 Tesla MR scanner. Contours obtained from the semi-automated segmentation were compared against manually segmented hip contours using Dice Ratio (DR), Hausdorff Distance (HD) and Root Mean Square (RMS) distance. The proposed method gave values of (DR = 0.84 ± 0.5, HD =3.0 ± 0.7, RMS =1.9 ± 0.3) and (DR=0.86 ± 0.2, HD=3.0 ± 0.1, RMS= 2.0 ± 0.6) for right and left acetabular contours respectively which was higher than the corresponding values obtained from conventional RW segmentation. The execution time of the segmentation algorithm was less than ~4 seconds on a 3.5 GHz CPU.

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In this paper, we propose a Compressive Sensing based approach to the problem of real-time reconstruction of MR image sequences. Our proposed method is able to extract useful priori information and incorporate it into a modified iterative thresholding algorithm for fast casual reconstruction of MR images from highly undersampled k-space data. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, while having a lower computational complexity and memory requirements compared to the other state-of-the-art methods.

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The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices.

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A novel segmentation-based image approximation and coding technique is proposed. A hybrid quad-binary (QB) tree structure is utilized to efficiently model and code geometrical information within images. Compared to other tree-based representation such as wedgelets, the proposed QB-tree based method is more efficient for a wide range of contour features such as junctions, corners and ridges, especially at low bit rates.