Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization

Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done. Uncertainty quantification (UQ) is essential for qualifying NDE inspections and building trust in their predictions. Therefore, this article aims to demonstrate how UQ can best be achieved for deep learning in the context of crack sizing for inline pipe inspection. A convolutional neural network architecture is used to size surface breaking defects from plane wave imaging (PWI) images with two modern UQ methods: deep ensembles and Monte Carlo dropout. The network is trained using PWI images of surface breaking defects simulated with a hybrid finite element / ray-based model. Successful UQ is judged by calibration and anomaly detection, which refer to whether in-domain model error is proportional to uncertainty and if out of training domain data is assigned high uncertainty. Calibration is tested using simulated and experimental images of surface breaking cracks, while anomaly detection is tested using experimental side-drilled holes and simulated embedded cracks. Monte Carlo dropout demonstrates poor uncertainty quantification with little separation between in and out-of-distribution data and a weak linear fit ( R=0.84 ) between experimental root-mean-square-error and uncertainty. Deep ensembles improve upon Monte Carlo dropout in both calibration ( R=0.95 ) and anomaly detection. Adding spectral normalization and residual connections to deep ensembles slightly improves calibration ( R=0.98 ) and significantly improves the reliability of assigning high uncertainty to out-of-distribution samples

Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1–5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing

Deep learning for ultrasonic crack characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing

The epiphyseal scar: changing perceptions in relation to skeletal age estimation.

BACKGROUND: It is imperative that all methods applied in skeletal age estimation and the criteria on which they are based have a strong evidential basis. The relationship between the persistence of epiphyseal scars and chronological age, however, has remained largely untested. AIMS: To assess the relationships between the level of persistence of the epiphyseal scar and chronological age, biological sex and side of the body in relation to the interpretation of epiphyseal scars in methods of skeletal age estimation. SUBJECTS AND METHODS: A sample of radiographic images was obtained from the Tayside NHS Trust, Ninewells Hospital, Dundee, UK. This included images of four anatomical regions from living female and male individuals aged between 20-50 years. RESULTS: Some remnant of an epiphyseal scar was found in 78-99% of individuals examined in this study. The level of persistence of epiphyseal scars was also found to vary between anatomical regions. CONCLUSION: The overall relationship between chronological age and the level of persistence or obliteration of the epiphyseal scar was found to be of insufficient strength to support a causative link. It is, therefore, necessary that caution is employed in their interpretation in relation to skeletal age estimation practices

Specific Loss of Histone H3 Lysine 9 Trimethylation and HP1γ/Cohesin Binding at D4Z4 Repeats Is Associated with Facioscapulohumeral Dystrophy (FSHD)

Facioscapulohumeral dystrophy (FSHD) is an autosomal dominant muscular dystrophy in which no mutation of pathogenic gene(s) has been identified. Instead, the disease is, in most cases, genetically linked to a contraction in the number of 3.3 kb D4Z4 repeats on chromosome 4q. How contraction of the 4qter D4Z4 repeats causes muscular dystrophy is not understood. In addition, a smaller group of FSHD cases are not associated with D4Z4 repeat contraction (termed “phenotypic” FSHD), and their etiology remains undefined. We carried out chromatin immunoprecipitation analysis using D4Z4–specific PCR primers to examine the D4Z4 chromatin structure in normal and patient cells as well as in small interfering RNA (siRNA)–treated cells. We found that SUV39H1–mediated H3K9 trimethylation at D4Z4 seen in normal cells is lost in FSHD. Furthermore, the loss of this histone modification occurs not only at the contracted 4q D4Z4 allele, but also at the genetically intact D4Z4 alleles on both chromosomes 4q and 10q, providing the first evidence that the genetic change (contraction) of one 4qD4Z4 allele spreads its effect to other genomic regions. Importantly, this epigenetic change was also observed in the phenotypic FSHD cases with no D4Z4 contraction, but not in other types of muscular dystrophies tested. We found that HP1γ and cohesin are co-recruited to D4Z4 in an H3K9me3–dependent and cell type–specific manner, which is disrupted in FSHD. The results indicate that cohesin plays an active role in HP1 recruitment and is involved in cell type–specific D4Z4 chromatin regulation. Taken together, we identified the loss of both histone H3K9 trimethylation and HP1γ/cohesin binding at D4Z4 to be a faithful marker for the FSHD phenotype. Based on these results, we propose a new model in which the epigenetic change initiated at 4q D4Z4 spreads its effect to other genomic regions, which compromises muscle-specific gene regulation leading to FSHD pathogenesis

The persistence of epiphyseal scars in the distal radius in adult individuals

The use of radiographic imaging in the estimation of chronological age facilitates the analysis of structures not visible on gross morphological inspection. Following the completion of epiphyseal fusion, a thin radio-opaque band, the epiphyseal scar, may be observed at the locus of the former growth plate. The obliteration of this feature has previously been interpreted as the final stage of skeletal maturation and consequently has been included as a criterion in several methods of age estimation, particularly from the distal radius. Due to the recommendations relating to age estimation in living individuals, accurate assessment of age from the distal radius is of great importance in human identification; however, the validity of the interpretation of the obliteration of the epiphyseal scar as an age-related process has not been tested. A study was undertaken to assess the persistence of epiphyseal scars in adults between 20 and 50 years of age through the assessment of 616 radiographs of left and right distal radii from a cross-sectional population. This study found that 86 % of females and 78 % of males retained some remnant of the epiphyseal scar in the distal radius. The relationships between chronological age, biological sex and the persistence of the epiphyseal scar were not statistically significant. The findings of this study indicate that the epiphyseal scars may persist in adult individuals until at least 50 years of age. No maximum age should therefore be applied to the persistence of an epiphyseal scar in the distal radius

Stage-I osteochondritis dissecans versus normal variants of ossification in the knee in children

Background: Juvenile osteochondritis dissecans (OCD) has a better prognosis than the adult type. Objective : We postulated that the excellent prognosis of juvenile OCD could be explained, at least in part, by the erroneous diagnosis of some developmental variants of ossification as stage-I OCD. Materials and methods : Knee MRIs of 38 children, ages 7.5–17.7 years (mean and median age 13 years), were retrospectively reviewed to look for features that might separate normal variants of ossification from stage-I OCD. These included age, gender, site, configuration of the lesion, residual cartilaginous model and presence of edema. Results : Twenty-three patients (32 condyles) had ossification defects with intact articular cartilage suggestive of stage-I lesions. No stage-II lesions were seen in the posterior femoral condyles. Accessory ossification centers were seen in 11/16 posterior condyles and 3/16 central condyles. Spiculation of existing ossification was seen in 12/16 posterior condylar lesions and 1/16 central condyles. There was a predominance of accessory ossifications and spiculations in the patients with 10% or greater residual cartilaginous model. No edema signal greater than diaphyseal red-marrow signal was seen in the posterior condyles. Clinical follow-up ranged from 0.5 to 38 months, with clinical improvement in 22 out of 23 patients. Conclusion : Inclusion of normal variants in the stage-I OCD category might explain, in part, the marked difference in published outcome between the juvenile and adult forms of OCD. Ossification defects in the posterior femoral condyles with intact overlying articular cartilage, accessory ossification centers, spiculation, residual cartilaginous model, and lack of bone-marrow edema are features of developmental variants rather than OCD.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46719/1/247_2005_Article_1507.pd

Enigma proteins regulate YAP mechanotransduction

Human cells can sense mechanical stress acting upon integrin adhesions and respond by sending the YAP (also known as YAP1) and TAZ (also known as WWTR1) transcriptional co-activators to the nucleus to drive TEAD-dependent transcription of target genes. How integrin signaling activates YAP remains unclear. Here, we show that integrin-mediated mechanotransduction requires the Enigma and Enigma-like proteins (PDLIM7 and PDLIM5, respectively; denoted for the family of PDZ and LIM domain-containing proteins). YAP binds to PDLIM5 and PDLIM7 (hereafter PDLIM5/7) via its C-terminal PDZ-binding motif (PBM), which is essential for full nuclear localization and activity of YAP. Accordingly, silencing of PDLIM5/7 expression reduces YAP nuclear localization, tyrosine phosphorylation and transcriptional activity. The PDLIM5/7 proteins are recruited from the cytoplasm to integrin adhesions and F-actin stress fibers in response to force by binding directly to the key stress fiber component α-actinin. Thus, forces acting on integrins recruit Enigma family proteins to trigger YAP activation during mechanotransduction.This article has an associated First Person interview with the first author of the paper