Over the last decades, facing the blooming growth of technological progress, interest in digital devices such as computed tomography (CT) as well as magnetic resource imaging which emerged in the 1970s has continued to grow. Such medical data can be invested in numerous visual recognition applications. In this context, these data may be segmented to generate a precise 3D representation of an organ that may be visualized and manipulated to aid surgeons during surgical interventions. Notably, the segmentation process is performed manually through the use of image processing software. Within this framework, multiple outstanding approaches were elaborated. However, the latter proved to be inefficient and required human intervention to opt for the segmentation area appropriately. Over the last few years, automatic methods which are based on deep learning approaches have outperformed the state-of-the-art segmentation approaches due to the use of the relying on Convolutional Neural Networks. In this paper, a segmentation of preoperative patients CT scans based on deep learning architecture was carried out to determine the target organ’s shape. As a result, the segmented 2D CT images are used to generate the patient-specific biomechanical 3D model. To assess the efficiency and reliability of the proposed approach, the 3DIRCADb dataset was invested. The segmentation results were obtained through the implementation of a U-net architecture with good accuracy.

With the emergence of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), three-dimensional images facilitate the generation of 3D models of a patient, providing a new practical and accurate assistance, particularly for surgical planning. These images can be manipulated to produce an accurate 3D representation of an organ. The reconstructed mesh can be used to generate and visualize a deformable model during surgical intervention using Augmented Reality (AR) technology. To obtain an efficient reconstruction, a segmentation of these medical images using deep learning architecture can be used to extract the target organ's properties. Many methods were proposed based on the captured pre-operative patient's CT scans. Generally, the segmentation process is done manually using image processing software. In this context several approaches were proposed, these methods are not efficient and need human interaction to select the segmentation area correctly. This work aims to develop a deep learning method using a Convolutional Neural Network (CNN) that captures the liver organ from a set of CT scans. Given preoperative patient-specific data (CT scans), the U-net architecture is implemented to detect the liver organ. As a result, the segmented 2D images are used to generate a 3D patient-specific liver model.

Augmented Reality (AR) technology offers innovative ways in order to visualize and manipulate a 3D model of an object by superimposing computer-generated images onto another object interactively. The ability to interact with digital and spatial information in real-time offers new opportunities to manipulate and process medical data easily and efficiently. During surgical interventions, surgeons face various challenges dealing with digital patient data. Several methods are used to visualize the operative areas, such as fluoroscopy and ultrasound techniques. These techniques have several limitations. Thus, the augmented reality technique could serve as a better alternative to project a three-dimensional model of the target organ into the surgeon's perspective and field of view to improve the accuracy and efficiency of the medical intervention intraoperatively. In this paper, a new AR method is proposed in order to visualize and simulate the biomechanical model of the liver organ during open hepatic surgery. In this regard, the 3D model based on the patient's preoperative CT scans is first reconstructed. Then, the reconstructed model is projected using the AR headset. After that, the biomechanical model is generated and prepared for the simulation. The proposed approach is validated using acquired CT scans of the human organ.

Reverse engineering (RE) consists in the reconstruction of a geometric model of a 3D object from a set of points, a mesh or a 3D triangulation. This model is a combination of geometric primitives (cylinders, planes, spheres, etc.) and complex surfaces (B-Spline, NURBS$\dots$) defined by parameters that can be estimated from the 3D data. RE is widely used in different fields such as mechanic, artistic, medical, Building Information Modeling, reality augmentation, etc. In this context, the reconstruction of 3D surface is an important task to obtain the Computer Aided Design (CAD) model in order to visualize 3D objects and approximate their shapes by mathematical formulations. Triangular surface models are now commonly used to model three-dimensional object. Many of these geometric models are obtained from scanning techniques or modeled through CAD software. This paper presents a new approach to rebuild a CAD model basing on the reconstruction of the B-Spline surfaces given a set of points extracted from a deformed mesh. To guarantee a good precision of the fitted surface, new 3D points are inserted to the input mesh using the Walton's method. Given the updated set of points, the B-Spline surface is approximated. To validate the proposed method, reconstruction errors of different complex 3D surfaces before and after the points insertion are calculated. A comparison with the existing methods prove the efficiency of the developed algorithm.

According to its causes and effects, it becomes essential to judge the quality of the model after a treatment, compared to the initial image. In fact, the quality of 3D objects becomes a paramount criterion for any treatment. Many authors proposed descriptors to evaluate the quality or the natural appearance of images. However, it is obvious that the best correlation between the results obtained and human visual perception. We stand out against current trends by avoiding purely developing and mathematical measures, or completely inspired by the HVS (Human Visual System). Indeed our new metric 3DrwPSNR based on the use of Weber's law that takes into account the human visual system. This law translates a logarithmic eye’s perception of light. This property led us to develop a metric that takes into consideration the relative difference of models and not the absolute difference. These measurements prove there are much correlated with the human visual appreciation of the processed 3D objects.