Computer-aided planning and reconstruction of cranial 3D implants

A Stage-Wise Residual Attention Generation Adversarial Network for Mandibular Defect Repairing and Reconstruction

Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The repairing and reconstruction of mandible defects is a significant yet challenging task in oral-maxillofacial surgery. Currently, the mainly available methods are traditional digitalized design methods that suffer from substantial artificial operations, limited applicability and high reconstruction error rates. An automated, precise, and individualized method is imperative for maxillofacial surgeons. In this paper, we propose a Stage-wise Residual Attention Generative Adversarial Network (SRA-GAN) for mandibular defect reconstruction. Specifically, we design a stage-wise residual attention mechanism for generator to enhance the extraction capability of mandibular remote spatial information, making it adaptable to various defects. For the discriminator, we propose a multi-field perceptual network, consisting of two parallel discriminators with different perceptual fields, to reduce the cumulative reconstruction errors. Furthermore, we design a self-encoder perceptual loss function to ensure the correctness of mandibular anatomical structures. The experimental results on a novel custom-built mandibular defect dataset demonstrate that our method has a promising prospect in clinical application, achieving the best Dice Similarity Coefficient (DSC) of 94.238% and 95% Hausdorff Distance (HD95) of 4.787.

3D printing in aortic endovascular therapies

Endovascular treatment of aortic disease, including aneurysm or dissection, is expanding at a rapid pace. Often, the specific patient anatomy in these cases is complex. Additive manufacturing, also known as three-dimensional (3D) printing, is especially useful in the treatment of aortic disease, due to its ability to manufacture physical models of complex patient anatomy. Compared to other surgical procedures, endovascular aortic repair can readily exploit the advantages of 3D printing with regard to operative planning and preoperative training. To date, there have been numerous uses of 3D printing in the treatment of aortic pathology as an adjunct in presurgical planning and as a basis for training modules for fellows and residents. In this review, we summarize the current uses of 3D printing in the endovascular management of aortic disease. We also review the process of producing these models, the limitations of their applications, and future directions of 3D printing in this field.

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.

A review on patient-specific facial and cranial implant design using Artificial Intelligence (AI) techniques

INTRODUCTION

Researchers and engineers have found their importance in healthcare industry including recent updates in patient-specific implant (PSI) design. CAD/CAM technology plays an important role in the design and development of Artificial Intelligence (AI) based implants.The across the globe have their interest focused on the design and manufacturing of AI-based implants in everyday professional use can decrease the cost, improve patient's health and increase efficiency, and thus many implant designers and manufacturers practice.

AREAS COVERED

The focus of this study has been to manufacture smart devices that can make contact with the world as normal people do, understand their language, and learn to improve from real-life examples. Machine learning can be guided using a heavy amount of data sets and algorithms that can improve its ability to learn to perform the task. In this review, artificial intelligence (AI), deep learning, and machine-learning techniques are studied in the design of biomedical implants.

EXPERT OPINION

The main purpose of this article was to highlight important AI techniques to design PSIs. These are the automatic techniques to help designers to design patient-specific implants using AI algorithms such as deep learning, machine learning, and some other automatic methods.

Three-dimensional (3D) printing and its applications for aortic diseases

Three-dimensional (3D) printing is a process which generates prototypes from virtual objects in computer-aided design (CAD) software. Since 3D printing enables the creation of customized objects, it is a rapidly expanding field in an age of personalized medicine. We discuss the use of 3D printing in surgical planning, training, and creation of devices for the treatment of aortic diseases. 3D printing can provide operators with a hands-on model to interact with complex anatomy, enable prototyping of devices for implantation based upon anatomy, or even provide pre-procedural simulation. Potential exists to expand upon current uses of 3D printing to create personalized implantable devices such as grafts. Future studies should aim to demonstrate the impact of 3D printing on outcomes to make this technology more accessible to patients with complex aortic diseases.

Computer-aided implant design for the restoration of cranial defects

Patient-specific cranial implants are important and necessary in the surgery of cranial defect restoration. However, traditional methods of manual design of cranial implants are complicated and time-consuming. Our purpose is to develop a novel software named EasyCrania to design the cranial implants conveniently and efficiently. The process can be divided into five steps, which are mirroring model, clipping surface, surface fitting, the generation of the initial implant and the generation of the final implant. The main concept of our method is to use the geometry information of the mirrored model as the base to generate the final implant. The comparative studies demonstrated that the EasyCrania can improve the efficiency of cranial implant design significantly. And, the intra- and inter-rater reliability of the software were stable, which were 87.07 ± 1.6% and 87.73 ± 1.4% respectively.

HTC Vive MeVisLab integration via OpenVR for medical applications

Virtual Reality, an immersive technology that replicates an environment via computer-simulated reality, gets a lot of attention in the entertainment industry. However, VR has also great potential in other areas, like the medical domain, Examples are intervention planning, training and simulation. This is especially of use in medical operations, where an aesthetic outcome is important, like for facial surgeries. Alas, importing medical data into Virtual Reality devices is not necessarily trivial, in particular, when a direct connection to a proprietary application is desired. Moreover, most researcher do not build their medical applications from scratch, but rather leverage platforms like MeVisLab, MITK, OsiriX or 3D Slicer. These platforms have in common that they use libraries like ITK and VTK, and provide a convenient graphical interface. However, ITK and VTK do not support Virtual Reality directly. In this study, the usage of a Virtual Reality device for medical data under the MeVisLab platform is presented. The OpenVR library is integrated into the MeVisLab platform, allowing a direct and uncomplicated usage of the head mounted display HTC Vive inside the MeVisLab platform. Medical data coming from other MeVisLab modules can directly be connected per drag-and-drop to the Virtual Reality module, rendering the data inside the HTC Vive for immersive virtual reality inspection.