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.

Computer-aided design has been widely used in modern industry for several decades, resulting in the huge databases of 3D CAD models that specialist companies currently own. Therefore, developing a solution to retrieve a reusable 3D CAD model becomes a strategic need for companies specializing in the modern design and manufacturing industry. Recently, some research works have been launched with the aim of recognizing 3D CAD objects based on design similarity and reusability. In this context, the use of probabilistic graphical models for information retrieval was always of great importance, especially when the context is characterized by the large volume of data and the uncertainty of the result. In this paper, authors will present a new approach proposed for modeling 3D CAD objects into reusable subparts. This approach is based on the Hidden Markov Model (HMM). This model has shown improved accuracy and efficiency in recognizing reusable 3D CAD objects, compared to other previously proposed solutions.

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.

Additive manufacturing techniques has great potential for manufacturing metal or polymer components with very high geometric complexity. This family of processes is now experiencing significant growth and is at the origin of intense research activity (optimization of topology, biomedical applications, etc.). One of the characteristics of this method is that the geometric complexity is free. The complexity of a CAD model is also a field of research. The basic idea is that the complexity of a component has implications in design and especially in manufacturing. Indeed, industrial competitiveness in the mechanical field generated the need to produce increasingly complex systems and parts (in terms of topology, functionality...). In the present work, we propose a complexity metric model based solely on the geometric information found in Computer-Aided Design (CAD) file. The proposed metric is a multiplicative model. Our investigation is based on the analysis of different parts picked from our technical document database. The first results of our work demonstrate that our model is highly correlated to a part's evaluated complexity. Nonetheless, with its current quality, our model could help engineering teams identify high-complexity products as early as the design phase.

With the technological advancement in the field of Computer Aided Design such as the rapid development of scanning technologies, the reconstruction of complete and incomplete cylinders given noisy point clouds with form defects becomes an important issue. In fact, cylindrical surfaces are found in domestic to industrial contexts. In this paper, a comparative study of cylinder fitting algorithms manufactured in the LIPPS laboratory is proposed. The aim of the proposed approach is to determine the diameter of cylindrical feature for minimizing roundness error from experimental data-points. The roundness error is evaluated using two internationally defined methods: Minimum Circumscribed Cylinder (MCC) and Maximum Inscribed Cylinder (MIC). All algorithms give similar results in the case where the scanned cylinder is complete and without form defects, but in the case of missing data some algorithms give unacceptable results. The two reference cylinders have been independently analyzed, respecting six criteria (calculation complexity, damping parameter, initial guess, time, circularity error and complexity cylinder). The results of algorithms are also compared to help manufacturers and inspectors facilitate and improve the application of these methods and to select the appropriate algorithm for size and form evaluation.

For several years, the reconstruction of Computer Aided Design (CAD) models from a deformed mesh get more and more attention. This CAD model is used in order to visualize 3D objects that were scanned and approximate their shapes by mathematical formulations. It represents the geometric support used in many other activities (analysis, manufacturing, assembly, etc.). Surface reconstruction is the most difficult problem of CAD model reconstruction. There are two types of surfaces: primitive surfaces and complex surfaces. In this paper, we propose a method to reconstruct complex surfaces. Our algorithm is based on Thin Plate Spline (TPS) method to optimize locations of control points of a B-Spline surface. Once surfaces are approximated, the geometric model can be reconstructed. We evaluate every step of our approach using mechanical models and show that we can achieve good results and meaningful approximated control points comparing with other methods.

Nowadays, recent technological advances in the acquisition, modeling and processing of three-dimensional (3D) objects data lead to the creation of models stored in huge databases, which are used in various domains such as computer vision, augmented reality, game industry, medicine, CAD (Computer-aided design), 3D printing etc. On the other hand, the industry is currently benefiting from powerful modeling tools enabling designers to easily and quickly produce 3D models. The great ease of acquisition and modeling of 3D objects make possible to create large 3D models databases, then, it becomes difficult to navigate them. Therefore, the indexing of 3D objects appears as a necessary and promising solution to manage this type of data, to extract model information, retrieve an existing model or calculate similarity between 3D objects. The objective of the proposed research is to develop a framework allowing easy and fast access to 3D objects in a CAD models database with specific indexing algorithm to find objects similar to a reference model. Our main objectives are to study existing methods of 3D objects similarity calculation (essentially shape-based methods) by specifying the characteristics of each method as well as the difference between them, and then we will propose a new approach for indexing and comparing 3D models, which is suitable for our case study and which is based on some studied previously methods. Our proposed approach is finally illustrated by an implementation, and evaluated in a professional context.

Geometric model reconstruction from a set of points is a difficult problem, which has been tackled with many different approaches. The reconstruction of the mechanical part is a necessity to visualize parts, simulate, assembly and detect interferences... The reconstruction of geometric entities (curves, edges, surfaces, faces) of these parts introduces particular difficulties. The most difficult problem to obtain the 3D geometric model from a cloud of points is the reconstruction of the faces. Many methods have been proposed to simplify the reconstruction of surfaces. There are two types of surface reconstruction: one of primitive shapes and the other of complex shapes like deformed mechanical parts and objects containing complex surfaces. In this paper, we present an algorithm to reconstruct the computer-aided design model from a deformed mesh. Then, we address a solution to reconstruct a 3D surface from a cloud of points extracted from a deformed mesh.