A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images

none6openFiorentino, Maria Chiara; Cipolletta, Edoardo; Filippucci, Emilio; Grassi, Walter; Frontoni, Emanuele; Moccia, SaraFiorentino, Maria Chiara; Cipolletta, Edoardo; Filippucci, Emilio; Grassi, Walter; Frontoni, Emanuele; Moccia, Sar

ADDRESSING PARTIAL VOLUME ARTIFACTS WITH QUANTITATIVE COMPUTED TOMOGRAPHY-BASED FINITE ELEMENT MODELING OF THE HUMAN PROXIMAL TIBIA

Quantitative computed tomography (QCT) based finite element modeling (FE) has potential to clarify the role of subchondral bone stiffness in osteoarthritis. The limited spatial resolution of clinical CT systems, however, results in partial volume (PV) artifacts and low contrast between the cortical and trabecular bone, which adversely affect the accuracy of QCT-FE models. Using different cortical modeling and partial volume correction algorithms, the overall aim of this research was to improve the accuracy of QCT-FE predictions of stiffness at the proximal tibial subchondral surface. For Study #1, QCT-FE models of the human proximal tibia were developed by (1) separate modeling of cortical and trabecular bone (SM), and (2) continuum models (CM). QCT-FE models with SM and CM explained 76%-81% of the experimental stiffness variance with error ranging between 11.2% and 20.2%. SM did not offer any improvement relative to CM. The segmented cortical region indicated densities below the range reported for cortical bone, suggesting that cortical voxels were corrupted by PV artifacts. For Study #2, we corrected PV layers at the cortical bone using four different methods including: (1) Image Deblurring of all of the proximal tibia (IDA); (2) Image Deblurring of the cortical region (IDC); (3) Image Remapping (IR); and (4) Voxel Exclusion (VE). IDA resulted in low predictive accuracy with R2=50% and error of 76.4%. IDC explained 70% of the measured stiffness variance with 23.3% error. The IR approach resulted in an R2 of 81% with 10.6% error. VE resulted in the highest predictive accuracy with R2=84%, and 9.8% error. For Study #3, we investigated whether PV effects could be addressed by mapping bone’s elastic modulus (E) to mesh Gaussian points. Corresponding FE models using the Gauss-point method converged with larger elements when compared to the conventional method which assigned a single elastic modulus to each element (constant-E). The error at the converged mesh was similar for constant-E and Gauss-point; though, the Gauss-point method indicated this error with larger elements and less computation time (30 min vs 180 min). This research indicated that separate modeling of cortical and trabecular bone did not improve predictions of stiffness at the subchondral surface. However, this research did indicate that PV correction has potential to improve QCT-FE models of subchondral bone. These models may help to clarify the role of subchondral bone stiffness in knee OA pathogenesis with living people

K-means Clustering In Knee Cartilage Classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model

Three-dimensional Ultrasound Imaging For Quantifying Knee Cartilage Volume

Arthritis is the most common chronic health condition in Canada, with the most common form being osteoarthritis (OA). There is a great clinical need for an objective imaging-based point-of-care tool to assess OA status, progression, and response to treatment. This thesis aims to validate a handheld mechanical three-dimensional (3D) ultrasound (US) device against the current clinical standard of magnetic resonance imaging (MRI) for quantifying femoral articular cartilage (FAC) volume. Knee images of 25 healthy volunteers were acquired using 3D US and 3.0 Tesla MRI scans. Two raters manually segmented the trochlear FAC during separate sessions to assess intra- and inter-rater reliabilities. The results demonstrated that 3D US has excellent reliability and strong concurrent validity with MRI for measuring healthy FAC volume. 3D US is a promising, inexpensive, and widely accessible imaging modality that will enable clinicians and researchers to obtain additional information without added complexity or discomfort to patients

Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net

Artificial intelligence in musculoskeletal ultrasound imaging

Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.11Nsciescopu

Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis

Damage to cartilage is an important indicator of osteoarthritis progression, but manual extraction of cartilage morphology is time-consuming and prone to error. To address this, we hypothesize that automatic labeling of cartilage can be achieved through the comparison of contrasted and non-contrasted Computer Tomography (CT). However, this is non-trivial as the pre-clinical volumes are at arbitrary starting poses due to the lack of standardized acquisition protocols. Thus, we propose an annotation-free deep learning method, D-net, for accurate and automatic alignment of pre- and post-contrasted cartilage CT volumes. D-Net is based on a novel mutual attention network structure to capture large-range translation and full-range rotation without the need for a prior pose template. CT volumes of mice tibiae are used for validation, with synthetic transformation for training and tested with real pre- and post-contrasted CT volumes. Analysis of Variance (ANOVA) was used to compare the different network structures. Our proposed method, D-net, achieves a Dice coefficient of 0.87, and significantly outperforms other state-of-the-art deep learning models, in the real-world alignment of 50 pairs of pre- and post-contrasted CT volumes when cascaded as a multi-stage network

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Ultrasonography (US) has become a valuable imaging tool for the examination of the musculoskeletal system. It provides important diagnostic information and it can also be very useful in the assessment of disease activity and treatment response. US has gained widespread use in rheumatology practice because it provides real time and dynamic assessment, although it is dependent on the examiner’s experience. The implementation of artificial intelligence (AI) techniques in the process of image recognition and interpretation has the potential to overcome certain limitations related to physician-dependent assessment, such as the variability in image acquisition. Multiple studies in the field of AI have explored how integrated machine learning algorithms could automate specific tissue recognition, diagnosis of joint and muscle pathology, and even grading of synovitis which is essential for monitoring disease activity. AI-based techniques applied in musculoskeletal US imaging focus on automated segmentation, image enhancement, detection and classification. AI-based US imaging can thus improve accuracy, time efficiency and offer a framework for standardization between different examinations. This paper will offer an overview of current research in the field of AI-based ultrasonography of the musculoskeletal system with focus on the applications of machine learning techniques in the examination of joints, muscles and peripheral nerves, which could potentially improve the performance of everyday clinical practice