Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams

A diagnostic imaging technique and therapeutic strategy for early osteoarthritis

Thesis (Ph.D.)--Boston UniversityOsteoarthritis (OA) is a chronic, progressive disease of diarthrodial joints arising from the breakdown of articular cartilage. As one of the leading causes of disability and lifestyle limitations in the United States, osteoarthritis is estimated to affect 27 million people in the U.S. and cost the economy $128 billion annually. Current diagnostic techniques detect OA only in its later stages, when irreversible cartilage damage has already occurred. A reliable, non-invasive method for diagnosing OA in its early stages would provide an opportunity to intervene and potentially to stay disease progression. Likewise, the field of OA research would benefit from a technique that allows tissue engineering and small molecule therapies to be evaluated longitudinally. Contrast-enhanced computed tomography (CECT) of cartilage is a developing medical imaging technique for evaluating cartilage biochemical and biomechanical properties. CECT has been shown to accurately quantify measures of cartilage integrity such as glycosaminoglycan (GAG) content, equilibrium compressive modulus, and coefficients of friction. In the studies presented herein, cationic iodinated contrast agents are developed for quantitative cartilage CECT, a technique predicated on the diffusion and partitioning of a charged contrast agent into the cartilage. The experiments show that cationic contrast agents lack specific interactions with anionic GAGs and are highly taken up in cartilage due, instead, to their electrostatic attraction. At diffusion equilibrium, both anionic and cationic agents indicate GAG content and biomechanical properties as measured by microcomputed tomography, though cationic contrast agents were found to diffuse through cartilage more slowly than anionic ones. Translating CECT to intact joints with clinically available helical CT scanners bears promising results, but concerns remain regarding in vivo applicability. Anionic contrast agents enable GAG content quantification following brief contrast agent exposure, whereas cationic agents require full equilibration within the tissue. To explore treatment modalities for early OA, a novel interpenetrating hydrogel method was developed to reconstitute the mechanical properties of cartilage models for early OA. Preliminary results show that the interpenetrating network strengthened cartilage with respect to compressive loading suggesting that the treatment could potentially serve as a functional replacement for GAG lost in the early stages of OA

3D quantification of osteoclast resorption of equine bone in vitro

Des charges cycliques élevées induisent la formation de microfissures dans l'os, déclenchant un processus de remodelage ciblé, mené par les ostéoclastes et suivi par les ostéoblastes, visant à réparer et à prévenir l'accumulation des dommages. L'os de cheval de course est un modèle idéal pour étudier les effets d'une charge de haute intensité, car il est sujet à une accumulation focale de microfissures et à la résorption qui s'ensuit dans les articulations. Les ostéoclastes équins ont rarement été étudiés in vitro. Le volume de résorption des ostéoclastes est considéré comme un paramètre direct de l'activité des ostéoclastes, mais des méthodes indirectes de quantification en 2D de la résorption osseuse sont plus souvent utilisées. L'objectif de cette étude était de développer une méthode précise, à haut débit et assistée par l'apprentissage profond pour quantifier le volume de résorption des ostéoclastes équins dans les images micro tomodensitométrie (µCT) 3D. Des ostéoclastes équins ont été cultivés sur des tranches d'os équins, imagés par μCT avant et après la culture. Le volume, le ratio de forme et la profondeur maximale de chaque événement de résorption ont été mesurés dans les images volumétriques de trois tranches d'os. Un convolution neural network (CNN) a ensuite été entraîné à identifier les événements de résorption sur les images μCT post-culture, puis le modèle a été appliqué à des tranches d'os d'archives (n=21), pour lesquelles l’aire de résorption en 2D, et la concentration du biomarqueur de résorption CTX-I étaient connues. Cela a permis d'obtenir des informations 3D sur la résorption des tranches d’os pour lesquels aucune imagerie n'avait été réalisée avant la mise en culture. La valeur modale du volume, la profondeur maximale et le ratio de forme des événements de résorption discrète étaient respectivement de 2,7*103µm3, 12 µm et 0,18. Le volume de résorption moyen par tranche d'os archivés était de 34155,34*103µm3. Le volume de résorption mesuré par le CNN était en forte corrélation avec les mesures de CTX-I (p <0,001) et d’aire (p <0,001). Cette technique de segmentation des images µCT des coupes osseuses assistée par apprentissage profond pour quantifier le volume de résorption osseuse des ostéoclastes équins permettra des recherches futures plus précises et plus approfondies sur l'activité des ostéoclastes. Par exemple, les effets antirésorptifs de médicaments tels que les corticostéroïdes et les bisphosphonates pourront être étudiés à l'avenir.High cyclic loads induce the formation of microcracks in bone, initiating a process of targeted remodeling, led by osteoclasts, and followed by osteoblasts, aimed at repairing and preventing accumulation of damage. Racehorse bone is an ideal model for studying the effects of high-intensity loading, as it is subject to focal accumulation of microcracks and subsequent resorption within joints. Equine osteoclasts have rarely been investigated in vitro. The volume of osteoclast resorption is considered a direct parameter of osteoclast activity, but indirect 2D quantification methods are used more often. The objective of this study was to develop an accurate, high-throughput, deep learning-aided method to quantify equine osteoclast resorption volume in µCT 3D images. Equine osteoclasts were cultured on equine bone slices, imaged with μCT pre- and post-culture. Volume, aspect ratio (shape factor) and maximum depth of each resorption event were measured in volumetric images of three bone slices. A convolutional neural network (U-Net-like) was then trained to identify resorption events on post-culture μCT images and then the network was applied to archival bone slices (n=21), for which the area of resorption in 2D, and the concentration of a resorption biomarker CTX-I were known. This unlocked the 3D information on resorption for bone slices where no pre-culture imaging was done. The modal volume, maximum depth, and aspect ratio of discrete resorption events were 2.7*103µm3, 12 µm and 0.18 respectively. The mean resorption volume per bone slice on achieved bone samples was 34155.34*103µm3. The CNN-labeled resorption volume correlated strongly with both CTX-I (p <0.001) and area measurements (p <0.001). This technique of deep learning-aided feature segmentation of µCT images of bone slices for quantifying equine osteoclast bone resorption volume allows for more accurate and extensive future investigations on osteoclast activity. For example, the antiresorptive effects of medications like corticosteroids and bisphosphonates can be investigated in the future

A Review on Segmentation of Knee Articular Cartilage: from Conventional Methods Towards Deep Learning

In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that the state-of-the-art techniques based on DL outperforms the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches have achieved the best results (mean Dice similarity cofficient (DSC) between 85:8% and 90%)

Knee complaints and prognosis of osteoarthritis at 10 years : impact of ACL ruptures, meniscal tears, genetic predisposition and surgery

In this thesis we demonstrated that several known risk factors for knee OA development i.e. ACL ruptures, meniscal tears, the presence of hand OA and increased BMI, are already associated with knee OA development as demonstrated on radiographs and MR images early in life. Identifying these factors in young to middle aged patients suffering from knee complaints helps to define high risk patients who may benefit from early preventive exercise therapy or maybe disease modifying drugs which might be developed in the future. Meniscectomy and ACL reconstruction have no effect on knee OA development after 10 years in patients with sub-acute knee complaints. The in this thesis validated automatic JSW quantification method is sensitive to small changes in JSW of the finger joints. The first signs of hand OA development with this method can be detected within one or two years. In patients with traumatic meniscal tears but without knee locking symptoms, there may be some benefits from treatment with meniscectomy in long-term Sports and Recreation knee function outcomes compared to conservative treatment. Future randomized controlled trials may elucidate the effect of surgical treatment of traumatic meniscal tears.ReumafondsUBL - phd migration 201

Novel Approaches to the Representation and Analysis of 3D Segmented Anatomical Districts

Nowadays, image processing and 3D shape analysis are an integral part of clinical practice and have the potentiality to support clinicians with advanced analysis and visualization techniques. Both approaches provide visual and quantitative information to medical practitioners, even if from different points of view. Indeed, shape analysis is aimed at studying the morphology of anatomical structures, while image processing is focused more on the tissue or functional information provided by the pixels/voxels intensities levels. Despite the progress obtained by research in both fields, a junction between these two complementary worlds is missing. When working with 3D models analyzing shape features, the information of the volume surrounding the structure is lost, since a segmentation process is needed to obtain the 3D shape model; however, the 3D nature of the anatomical structure is represented explicitly. With volume images, instead, the tissue information related to the imaged volume is the core of the analysis, while the shape and morphology of the structure are just implicitly represented, thus not clear enough. The aim of this Thesis work is the integration of these two approaches in order to increase the amount of information available for physicians, allowing a more accurate analysis of each patient. An augmented visualization tool able to provide information on both the anatomical structure shape and the surrounding volume through a hybrid representation, could reduce the gap between the two approaches and provide a more complete anatomical rendering of the subject. To this end, given a segmented anatomical district, we propose a novel mapping of volumetric data onto the segmented surface. The grey-levels of the image voxels are mapped through a volume-surface correspondence map, which defines a grey-level texture on the segmented surface. The resulting texture mapping is coherent to the local morphology of the segmented anatomical structure and provides an enhanced visual representation of the anatomical district. The integration of volume-based and surface-based information in a unique 3D representation also supports the identification and characterization of morphological landmarks and pathology evaluations. The main research contributions of the Ph.D. activities and Thesis are: \u2022 the development of a novel integration algorithm that combines surface-based (segmented 3D anatomical structure meshes) and volume-based (MRI volumes) information. The integration supports different criteria for the grey-levels mapping onto the segmented surface; \u2022 the development of methodological approaches for using the grey-levels mapping together with morphological analysis. The final goal is to solve problems in real clinical tasks, such as the identification of (patient-specific) ligament insertion sites on bones from segmented MR images, the characterization of the local morphology of bones/tissues, the early diagnosis, classification, and monitoring of muscle-skeletal pathologies; \u2022 the analysis of segmentation procedures, with a focus on the tissue classification process, in order to reduce operator dependency and to overcome the absence of a real gold standard for the evaluation of automatic segmentations; \u2022 the evaluation and comparison of (unsupervised) segmentation methods, finalized to define a novel segmentation method for low-field MR images, and for the local correction/improvement of a given segmentation. The proposed method is simple but effectively integrates information derived from medical image analysis and 3D shape analysis. Moreover, the algorithm is general enough to be applied to different anatomical districts independently of the segmentation method, imaging techniques (such as CT), or image resolution. The volume information can be integrated easily in different shape analysis applications, taking into consideration not only the morphology of the input shape but also the real context in which it is inserted, to solve clinical tasks. The results obtained by this combined analysis have been evaluated through statistical analysis

Accomplishments and challenges in stem cell imaging in vivo.

Stem cell therapies have demonstrated promising preclinical results, but very few applications have reached the clinic owing to safety and efficacy concerns. Translation would benefit greatly if stem cell survival, distribution and function could be assessed in vivo post-transplantation, particularly in patients. Advances in molecular imaging have led to extraordinary progress, with several strategies being deployed to understand the fate of stem cells in vivo using magnetic resonance, scintigraphy, PET, ultrasound and optical imaging. Here, we review the recent advances, challenges and future perspectives and opportunities in stem cell tracking and functional assessment, as well as the advantages and challenges of each imaging approach