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%)

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

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

A biomechanics-based articulation model for medical applications

Computer Graphics came into the medical world especially after the arrival of 3D medical imaging. Computer Graphics techniques are already integrated in the diagnosis procedure by means of the visual tridimensional analysis of computer tomography, magnetic resonance and even ultrasound data. The representations they provide, nevertheless, are static pictures of the patients' body, lacking in functional information. We believe that the next step in computer assisted diagnosis and surgery planning depends on the development of functional 3D models of human body. It is in this context that we propose a model of articulations based on biomechanics. Such model is able to simulate the joint functionality in order to allow for a number of medical applications. It was developed focusing on the following requirements: it must be at the same time simple enough to be implemented on computer, and realistic enough to allow for medical applications; it must be visual in order for applications to be able to explore the joint in a 3D simulation environment. Then, we propose to combine kinematical motion for the parts that can be considered as rigid, such as bones, and physical simulation of the soft tissues. We also deal with the interaction between the different elements of the joint, and for that we propose a specific contact management model. Our kinematical skeleton is based on anatomy. Special considerations have been taken to include anatomical features like axis displacements, range of motion control, and joints coupling. Once a 3D model of the skeleton is built, it can be simulated by data coming from motion capture or can be specified by a specialist, a clinician for instance. Our deformation model is an extension of the classical mass-spring systems. A spherical volume is considered around mass points, and mechanical properties of real materials can be used to parameterize the model. Viscoelasticity, anisotropy and non-linearity of the tissues are simulated. We particularly proposed a method to configure the mass-spring matrix such that the objects behave according to a predefined Young's modulus. A contact management model is also proposed to deal with the geometric interactions between the elements inside the joint. After having tested several approaches, we proposed a new method for collision detection which measures in constant time the signed distance to the closest point for each point of two meshes subject to collide. We also proposed a method for collision response which acts directly on the surfaces geometry, in a way that the physical behavior relies on the propagation of reaction forces produced inside the tissue. Finally, we proposed a 3D model of a joint combining the three elements: anatomical skeleton motion, biomechanical soft tissues deformation, and contact management. On the top of that we built a virtual hip joint and implemented a set of medical applications prototypes. Such applications allow for assessment of stress distribution on the articular surfaces, range of motion estimation based on ligament constraint, ligament elasticity estimation from clinically measured range of motion, and pre- and post-operative evaluation of stress distribution. Although our model provides physicians with a number of useful variables for diagnosis and surgery planning, it should be improved for effective clinical use. Validation has been done partially. However, a global clinical validation is necessary. Patient specific data are still difficult to obtain, especially individualized mechanical properties of tissues. The characterization of material properties in our soft tissues model can also be improved by including control over the shear modulus

Magnetic Resonance Imaging for the Functional Analysis of Tissues and Biomaterials

Articular cartilage provides mechanical load dissipation and lubrication between joints, and additionally provides protects from abrasion. At present, there are no treatments to cure or attenuate the degradation of cartilage. Early detection and the ability to monitor the progression of osteoarthritis is important for developing effective therapies. However, few reliable imaging biomarkers exist to detect cartilage disease before advanced degeneration in the tissue. One specialized MRI technique, termed displacements under applied loading by MRI (dualMRI), was developed to measure displacements and strain in musculoskeletal tissues, hydrogels and engineered constructs. However, deformation information does not directly describe spatial distributions of tissue properties (e.g. stiffness), which is critical to the understanding of disease progression. To achieve the stiffness measurement, we developed and validated an inverse modeling workflow that combined dualMRI, to directly measure intratissue deformation, with topology optimization in the application of heterogeneous (layered) materials representative of the complex gradient architecture of articular cartilage. We successfully reconstructed bi-layer stiffness from ideal displacements calculated from forward simulation as well as from experimental data measured from dualMRI. To monitor the progression of osteoarthritis, we measured and analyzed biomechanical changes of sheep stifle cartilage after meniscectomy. We found that 2nd principal strain and max shear strain in the femur contact region are sensitive to cartilage degeneration at different stages and compared to more conventional methods like quantitative MRI. To investigate the biomechanical changes in articular cartilage with defect and repair, we implanted decellularized cartilage implant into sheep cartilage defect and evaluate the repair results using quantitative MRI and dualMRI. We found that implants placed in joints demonstrated lower strains compared to joints with untreated defects

Hybrid materials for meniscus replacement in the knee

The meniscus is cartilage that not only prevents the bones in knee joints to grind together but acts as a joint stabiliser. Many athletes and older people suffer from meniscus tears and degeneration. Meniscal tear treatments have been through meniscal suture or by partial meniscectomy (removal). These treatments may cause changes in loading or decreased contact area and increased contact stress. Consequently, the ultimate result is a total meniscectomy that potentially leads to osteoarthritis (OA). These current surgical strategies have lower success rates in younger patients. There are no successful artificial meniscus replacement devices for young patients, therefore, new materials for meniscus replacement are required. Here, the aim was to develop a novel biomimetic meniscus device made of a silica/polytetrahydrofuran (SiO2/polyTHF) inorganic/organic hybrid material. The device is biomimetic in terms of its structural design, mechanical properties, and integration with the host tissue. The device should delay onset of OA. The hybrid has unique properties in that is a bouncy material which has comparable mechanical properties to knee cartilage. Two pot hybrid synthesis was used to synthesise the SiO2/polyTHF hybrid and casting mould was developed based on the shrinkage factor of the hybrid. The hybrid synthesis modifications were conducted by controlling compositions and drying processes. Biological fixation of the hybrid meniscus was achieved by titanium anchors with gyroid porous architecture which can provide initial mechanical fixation and secondary biological fixation on the tibia. The architecture was designed using Solidworks and Rhinoceros software and printed by the Additive Manufacturing technique of selective laser melting (SLM). Mechanical testing of the device included compression, cyclic loading, shear strength and long-term 90 days in-vitro mechanical testing, tribology against living bovine 2 cartilage, and cell studies. The results suggest that combination of hybrid and Ti gyroid has potential to be meniscus implant due to comparable mechanical properties, low friction coefficient, and non-cytotoxicity.Open Acces