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The diseases and injuries of the knee joint are the most common orthopedic disorders. Personalized knee models can be helpful in the process of early intervention and lasting treatment techniques development. Fully automatic reconstruction of knee joint anatomical structures from medical images (CT, MRI, ultrasound) remains a challenge. For this reason, most of state-of-the-art knee joint models contain simplifications such as representation of muscles and ligaments as line segments connecting two points which replace attachment areas. The paper presents algorithms for automatic detection of such points on knee CT images. This paper presents three approaches to automatic detection of ligaments and tendons attachment sites on the patients CT images: qualitative anatomical descriptions, analysis of bones curvature, and quantitative anatomical descriptions. Combinations of these approaches result in new automatic detection algorithms. Each algorithm exploits anatomical peculiarities of each attachment site, e.g., bone curvature and number of other attachments in a neighborhood of the site. The experimental dataset consisted of 26 anonymized CT sequences containing right and left knee joints in different resolutions. The proposed algorithms take into account bone surface curvatures and spatial differences in locations of medial and lateral parts of both knees. The algorithms for detection of quadriceps femoris, popliteus, biceps femoris tendons, and lateral collateral and medial collateral ligaments attachment sites are provided, as well as examples of their application. Two algorithms are validated by comparison with known statistics of ligaments lengths and also using ground truth annotations for anatomical landmarks approved by clinical experts. The algorithms simplify generation of patient-specific knee joint models demanded in personalized biomechanical models. The algorithms in the current implementation have two important limitations. First, the correctness of the produced results depends on the bones segmentation quality. Second, the presented algorithms detect a point of the attachment site, which is not necessarily its center. Therefore, manual correction of the attachment site location may be required for attachments with relatively large area.

Human knee is one of the most complex joints. Different reasons may lead to knee instability. A personalized mathematical model of the knee may improve both diagnostic procedure and knee surgery outcomes. Such models require accurate geometric representation of bones and attachment sites of ligaments and tendons. This paper addresses automatic segmentation of knee bones and detection of origins and insertions for tendons and ligaments. The approach is based on anatomical features of bones and landmarks of tendons/ligaments attachments on the CT images. It provides a tool for the design of patient-specific geometrical knee models.

Aortic valve disease accounts for 45% of deaths from heart valve diseases.% \cite{Coffey2015}. An appealing approach to treat aortic valve disease is surgical replacement of the valve leaflets based on chemically treated autologous pericardium. This procedure is attractive due to its low cost and high effectiveness. We aim to develop a computational technology for patient-specific assessment of reconstructed aortic valve function that can be used by surgeons at the preoperative stage. The framework includes automatic computer tomography image segmentation, mesh generation, simulation of valve leaflet deformation. The final decision will be based on uncertainty analysis and leaflet shape optimization. This paper gives a proof of concept of our methodology: simulation methods are presented and studied numerically.

We address the problem of patient-specific modeling of human knee joints. We describe the algorithm of finding ligaments attachment points based on a segmentation procedure and anatomical landmarks. The obtained coordinates of ligaments’ origin and insertion points are implemented into a simple OpenSim knee joint model. The model is used to study knee passive flexion. The obtained ligaments forces and strains are in the range of physiological values.

Generation of three-dimensional personalized geometric models of anatomical structures is an important process for many practical tasks: computer-aided diagnosis, treatment planning and numerical modeling in biomedical applications. Despite many efforts done by different research groups, automatic segmentation of organs still does not have any general solution. The main difficulties are caused by peculiarities of different medical imaging modalities, image variability (for the same modality) resulting from the wide range of imaging devices, noise and artifacts, large patient anatomical variability and overlapping of intensity ranges of neighboring anatomical structures. In this article, we propose segmentation method based on analysis of texture features and developed specially for segmentation of abdominal organs. Its main advantage is robustness to interpatient gray level and anatomical variability. The proposed method was validated on the patient data. The method implementation was accelerated using graphics processing unit (GPU).

Electrical activity of the heart muscles can be measured noninvasively on the body surface using electrocardiography (ECG) method. Numerical results of ECG modeling can be effectively used for diagnosis and treatment planning for cardiovascular diseases. In this paper we propose a method for personalized ECG modeling. One of the most important stages in this process is the generation of personalized torso voxel models, containing heart, lungs, fat, muscles and other organs of the abdominal cavity. Since abdomen segmentation is the most complicated and time consuming task, we propose an automated method for abdominal computed tomography scans (CT) segmentation, based on the texture analysis. We also present numerical analysis of the influence of some anatomical structures of the abdomen on the ECG modeling results.

In this work we develop and present automatic and semi-automatic user-guided methods and algorithms for patient-specific image segmentation and generation of discrete geometric models for several cardiovascular biomedical applications. A new technique for dynamic heart ventricles segmentation and mesh generation using dynamic contrast enhanced Computed Tomography images is presented in detail.

In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.

A technology for high-resolution efficient numerical modeling of bioimpedance measurements is considered that includes 3D image segmentation, adaptive unstructured tetrahedral mesh generation, finite-element discretization, and the analysis of simulation data. The first-order convergence of the proposed numerical methods on a series of unmatched meshes and roughly second order convergence on a series of nested meshes are shown. Sensitivity field distributions for a conventional tetrapolar, as well as eight- and ten-electrode measurement configurations are obtained.

In this work we propose several techniques for personalized model adaptation, including anthropometrical scaling, control point mapping and geometrical modifi cation of the body extremities positions. We compare our previous results of segmental bioimpedance analysis using the Visible Human Project (VHP) man model to the modified model with a corrected position of the arms. Bioelectrical impedance analysis (BIA) is commonly used for body composition and abdominal adiposity assessment. BIA is also used in monitoring body fluid re - distribution under various physiological and pathological conditions. The basics of the bioelectrical impedance theory are well covered in (10, 13). The conventional measurement technique involves two pairs of electrodes attached to the skin. One pair, labeled current carrying (CC) electrodes, is used for injecting electrical cur- rent into the body. The second pair, labeled pick-up (PU) electrodes, is used for measuring the electric potential difference. This difference divided by th e value of the injected current is called the transfer impedance. The computational analysis of the existing measurement schemes is essential for accurate data interpretation and the development of new electrode schemes. One of the approaches is based on the calculation of the relative contribution of the tis- sues and organs to the result of bioimpedance measurements of the particular body segment. In our previous work (3-5) we aimed at numerical modelling of BIA using the finite element method (FEM), construction and visualization of sensitivity fie ld distributions using anatomically accurate 3D models of the human body from the Visible Human Project (VHP) (16). A similar approach had been used earlier with partially segmented models of the human body (2, 11, 17).