Quality Assessment Of Ultrasound Images Research Articles

Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing.

Autonomous Ultrasound Image Quality Assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultrasound images contain many spurious artifacts, such as noise due to handheld probe positioning, errors in the selection of probe parameters and patient respiration during the procedure. Further, these images are highly variable in appearance with respect to the individual patient's physiology. We propose to use a deep Convolutional Neural Network (CNN), USQNet, which utilizes a Multi-scale and Local-to-Global Second-order Pooling (MS-L2GSoP) classifier to conduct the sonographer-like assessment of image quality. This classifier first extracts features at multiple scales to encode the inter-patient anatomical variations, similar to a sonographer's understanding of anatomy. Then, it uses second-order pooling in the intermediate layers (local) and at the end of the network (global) to exploit the second-order statistical dependency of multi-scale structural and multi-region textural features. The L2GSoP will capture the higher-order relationships between different spatial locations and provide the seed for correlating local patches, much like a sonographer prioritizes regions across the image. We experimentally validated the USQNet for a new dataset of the human urinary bladder ultrasound images. The validation involved first with the subjective assessment by experienced radiologists' annotation, and then with state-of-the-art CNN networks for US-IQA and its ablated counterparts. The results demonstrate that USQNet achieves a remarkable accuracy of 92.4% and outperforms the SOTA models by 3 - 14% while requiring comparable computation time.

Ultrasound: A novel alternative technique for cervical epidural space visualization-A pilot study.

Neuraxial ultrasound (US), a newer modality, can be used for neuraxial imaging, helping in visualizing and aiding in epidural space catheterization. The aim of this study was to evaluate the efficacy of the US for cervical epidural access and to determine the failure rate and complication associated with this technique. A prospective single-arm pilot study was conducted on 21 participants. The neuraxial US image quality assessment by Ultrasound Visibility Score (UVS), epidural space depth measurement by US and by conventional loss of resistance (LOR) technique, and post-procedure epidural catheter confirmation by real-time US were the study parameters. Any procedural complications or failure rate were recorded. The Kolmogorov-Smirnov test, paired-samples t-test, and Chi-square test were used for the statistical comparison. The pre-procedural UVS by the transverse interlaminar view (x/21) was 2.81 ± 1.94 and by the oblique paramedian sagittal view was 16.66 ± 2.39 with UVS being best in the paramedian oblique sagittal view (P- value < 0.05). The comparison of depth of the epidural space identified by USG and that by the LOR technique was statistically insignificant (P = 0.83). The average puncture attempts were 1.1 ± 0.3. Post-procedure US epidural catheter confirmation score (x/3) was 1.44 ± 0.44 with either epidural space expansion or microbubbles seen or both. The pilot study has successfully demonstrated the implication of US for visualizing and aiding in epidural space catheterization. Also, the failure rate and procedural complications were drastically minimized with the help of US as compared to the traditional blind technique.

Medical Ultrasound Image Quality Assessment for Autonomous Robotic Screening

Autonomous ultrasound scanning robots have attracted the attention of researchers, and the real-time quality assessment of ultrasound images is the key technology of them. Existing robot systems usually use pixel-level feature statistical methods such as grayscale, confidence map, etc. However, in clinical practice doctors’ evaluation of ultrasound image quality not only relies on the pixel quality, but also on the image content. In this study, we introduced the deep learning method to the quality assessment of medical breast ultrasound images to learn the doctors’ clinical evaluation standards. We collected 1205 breast ultrasound images of 533 patients and asked experienced doctors to score them. The ResNet18 with a shallow number of layers is adopted to extract features of ultrasound images, and the high-order feature coding model BCNN (Bilinear CNN) in fine-grained categorization is adopted to improve the accuracy of quality assessment. The consistency between the ultrasound image quality assessment results of our method and the doctors’ annotation results reached 0.842 by PLCC (Pearson Linear Correlation Coefficient). Compared with the confidence map method commonly used for quality assessment in the automatic ultrasound scanning robots, our method achieves a higher consistency with the evaluation results of doctors and provides a new image evaluation method and idea for the visual servo control framework of ultrasound autonomous scanning robots.

Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy.

Knee arthroscopy is a complex minimally invasive surgery that can cause unintended injuries to femoral cartilage or postoperative complications, or both. Autonomous robotic systems using real-time volumetric ultrasound (US) imaging guidance hold potential for reducing significantly these issues and for improving patient outcomes. To enable the robotic system to navigate autonomously in the knee joint, the imaging system should provide the robot with a real-time comprehensive map of the surgical site. To this end, the first step is automatic image quality assessment, to ensure that the boundaries of the relevant knee structures are defined well enough to be detected, outlined, and then tracked. In this article, a recently developed one-class classifier deep learning algorithm was used to discriminate among the US images acquired in a simulated surgical scenario on which the femoral cartilage either could or could not be outlined. A total of 38 656 2-D US images were extracted from 151 3-D US volumes, collected from six volunteers, and were labeled as "1" or as "0" when an expert was or was not able to outline the cartilage on the image, respectively. The algorithm was evaluated using the expert labels as ground truth with a fivefold cross validation, where each fold was trained and tested on average with 15 640 and 6246 labeled images, respectively. The algorithm reached a mean accuracy of 78.4% ± 5.0, mean specificity of 72.5% ± 9.4, mean sensitivity of 82.8% ± 5.8, and mean area under the curve of 85% ± 4.4. In addition, interobserver and intraobserver tests involving two experts were performed on an image subset of 1536 2-D US images. Percent agreement values of 0.89 and 0.93 were achieved between two experts (i.e., interobserver) and by each expert (i.e., intraobserver), respectively. These results show the feasibility of the first essential step in the development of automatic US image acquisition and interpretation systems for autonomous robotic knee arthroscopy.

A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes.

Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93.52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i.e., ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81.2% when input size is approximately 300 × 300.

Multi-task learning for quality assessment of fetal head ultrasound images.

It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subjective and depends on the clinical experience of sonographers. In order to deal with this challenge, we propose a new multi-task learning framework using a faster regional convolutional neural network (MF R-CNN) architecture for standard plane detection and quality assessment. MF R-CNN can identify the critical anatomical structure of the fetal head and analyze whether the magnification of the ultrasound image is appropriate, and then performs quality assessment of ultrasound images based on clinical protocols. Specifically, the first five convolution blocks of the MF R-CNN learn the features shared within the input data, which can be associated with the detection and classification tasks, and then extend to the task-specific output streams. In training, in order to speed up the different convergence of different tasks, we devise a section train method based on transfer learning. In addition, our proposed method also uses prior clinical and statistical knowledge to reduce the false detection rate. By identifying the key anatomical structure and magnification of the ultrasound image, we score the ultrasonic plane of fetal head to judge whether it is a standard image or not. Experimental results on our own-collected dataset show that our method can accurately make a quality assessment of an ultrasound plane within half a second. Our method achieves promising performance compared with state-of-the-art methods, which can improve the examination effectiveness and alleviate the measurement error caused by improper ultrasound scanning.

FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks.

The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality assessment (FUIQA) scheme is proposed to assist the implementation of US image quality control in the clinical obstetric examination. The proposed FUIQA is realized with two deep convolutional neural network models, which are denoted as L-CNN and C-CNN, respectively. The L-CNN aims to find the region of interest (ROI) of the fetal abdominal region in the US image. Based on the ROI found by the L-CNN, the C-CNN evaluates the image quality by assessing the goodness of depiction for the key structures of stomach bubble and umbilical vein. To further boost the performance of the L-CNN, we augment the input sources of the neural network with the local phase features along with the original US data. It will be shown that the heterogeneous input sources will help to improve the performance of the L-CNN. The performance of the proposed FUIQA is compared with the subjective image quality evaluation results from three medical doctors. With comprehensive experiments, it will be illustrated that the computerized assessment with our FUIQA scheme can be comparable to the subjective ratings from medical doctors.

‘Ceci n’est pas une échographie': a plea for quality assessment in prenatal ultrasound

‘Ceci n’est pas une échographie': a plea for quality assessment in prenatal ultrasound