A fast and robust patient specific Finite Element mesh registration technique: application to 60 clinical cases

Finite Element mesh generation remains an important issue for patient specific biomechanical modeling. While some techniques make automatic mesh generation possible, in most cases, manual mesh generation is preferred for better control over the sub-domain representation, element type, layout and refinement that it provides. Yet, this option is time consuming and not suited for intraoperative situations where model generation and computation time is critical. To overcome this problem we propose a fast and automatic mesh generation technique based on the elastic registration of a generic mesh to the specific target organ in conjunction with element regularity and quality correction. This Mesh-Match-and-Repair (MMRep) approach combines control over the mesh structure along with fast and robust meshing capabilities, even in situations where only partial organ geometry is available. The technique was successfully tested on a database of 5 pre-operatively acquired complete femora CT scans, 5 femoral heads partially digitized at intraoperative stage, and 50 CT volumes of patients' heads. The MMRep algorithm succeeded in all 60 cases, yielding for each patient a hex-dominant, Atlas based, Finite Element mesh with submillimetric surface representation accuracy, directly exploitable within a commercial FE software

Anatomical Mirroring: Real-time User-specific Anatomy in Motion Using a Commodity Depth Camera

International audienceThis paper presents a mirror-like augmented reality (AR) system to display the internal anatomy of a user. Using a single Microsoft V2.0 Kinect, we animate in real-time a user-specific internal anatomy according to the user’s motion and we superimpose it onto the user’s color map. The user can visualize his anatomy moving as if he was able to look inside his own body in real-time. A new calibration procedure to set up and attach a user-specific anatomy to the Kinect body tracking skeleton is introduced. At calibration time, the bone lengths are estimated using a set of poses. By using Kinect data as input, the practical limitation of skin correspondance in prior work is overcome. The generic 3D anatomical model is attached to the internal anatomy registration skeleton, and warped on the depth image using a novel elastic deformer, subject to a closest-point registration force and anatomical constraints. The noise in Kinect outputs precludes any realistic human display. Therefore, a novel filter to reconstruct plausible motions based onfixed length bones as well as realistic angular degrees of freedom (DOFs) and limits is introduced to enforce anatomical plausibility. Anatomical constraints applied to the Kinect body tracking skeleton joints are used to maximize the physical plausibility of the anatomy motion, while minimizing the distance to the raw data. At run-time,a simulation loop is used to attract the bones towards the raw data, and skinning shaders efficiently drag the resulting anatomy to the user’s tracked motion.Our user-specific internal anatomy model is validated by comparing the skeleton with segmented MRI images. A user study is established to evaluate the believability of the animated anatomy

Towards automation of forensic facial reconstruction

Forensic facial reconstruction is a blend of art and science thus computerizing the process leads to numerous solutions. However, complete automation remains a challenge. This research concentrates on automating the first phase of forensic facial reconstruction which is automatic landmark detection by model fitting and extraction of feature points. Detection of landmarks is a challenging task since the skull orientation in a 3D scanned data cloud is generally arbitrary and unknown. To address the issue, well defined skull and mandible models with known geometric structure, features and orientation are (1) aligned and (2) fit to the scanned data. After model fitting is complete, landmarks can be extracted, within reasonable tolerance, from the dataset. Several methods exist for automatic registration (alignment); however, most suffer ambiguity or require interaction to manage symmetric 3D objects. A new alternative 3D model to data registration technique is introduced which works successfully for both symmetric and non-symmetric objects. It takes advantage of the fact that the model and data have similar shape and known geometric features. Therefore, a similar canonical frame of reference can be developed for both model and data. Once the canonical frame of reference is defined, the model can be easily aligned to data by a euclidian transformation of its coordinate system. Once aligned, the model is scaled and deformed globally to accommodate the overall size the object and bring the model in closer proximity to the data. Lastly, the model is deformed locally to better fit the scanned data. With fitting completed, landmark locations on the model can be utilized to isolate and select corresponding landmarks in the dataset. The registration, fitting and landmark detection techniques were applied to a set of six mandible and three skull body 3D scanned datasets. Results indicate the canonical axes formulation is a good candidate for automatic registration of complex 3D objects. The alternate approach posed for deformation and surface fitting of datasets also shows promise for landmark detection when using well constructed NURBS models. Recommendations are provided for addressing the algorithms limitations and to improve its overall performance

Registration and Segmentation of Multimodality Images for Post Processing of Skeleton in Preclinical Oncology Studies

Advancements in medical imaging techniques provide biomedical researchers with quality anatomical and functional information inside preclinical subjects in the fields of cancer, osteopathic, cardiovascular, and neurodegenerative research. The throughput of the preclinical imaging studies is a critical factor which determines the pace of small animal medical research. The time involved in manual analysis of large amount of imaging data prior to data interpretation by the researcher, limits the number of studies in a time frame. In the proposed solution, an automated image segmentation method was used to segment individual vertebrae in mice. Individual vertebrae of MOBY atlas were manually segmented and registered to the CT data. The PET activity for L1-L5 vertebrae was measured by applying the CT registered atlas vertebrae ROI. The algorithm was tested on three datasets from a PET/CT bone metastasis study using 18F-NaF radiotracer. The algorithm was found to reduce the analysis time threefold with a potential to further reduce the automated analysis time by use of computer system with better specification to run the algorithm. The manual analysis value can vary each time the analysis is performed and is dependent on the individual performing the analysis. Also the error percent was recorded and showed an increasing trend as the analysis moves down the spine from skull to caudal vertebrae. This method can be applied to segment the rest of the bone in the CT data and act as the starting point for the registration of the soft tissues

High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers

Each year thousands of people suffer from various types of cranial injuries and require personalized implants whose manual design is expensive and time-consuming. Therefore, an automatic, dedicated system to increase the availability of personalized cranial reconstruction is highly desirable. The problem of the automatic cranial defect reconstruction can be formulated as the shape completion task and solved using dedicated deep networks. Currently, the most common approach is to use the volumetric representation and apply deep networks dedicated to image segmentation. However, this approach has several limitations and does not scale well into high-resolution volumes, nor takes into account the data sparsity. In our work, we reformulate the problem into a point cloud completion task. We propose an iterative, transformer-based method to reconstruct the cranial defect at any resolution while also being fast and resource-efficient during training and inference. We compare the proposed methods to the state-of-the-art volumetric approaches and show superior performance in terms of GPU memory consumption while maintaining high-quality of the reconstructed defects

Registration accuracy of the optical navigation system for image-guided surgery

Abstract. During the last decades, image-guided surgery has been a vastly growing method during medical operations. It provides a new opportunity to perform surgical operations with higher accuracy and reliability than before. In image-guided surgery, a navigation system is used to track the instrument’s location and orientation during the surgery. These navigation systems can track the instrument in many ways, the most common of which are optical tracking, mechanical tracking, and electromagnetic tracking. Usually, the navigation systems are used primarily in surgical operations located in the head and spine area. For this reason, it is essential to know the registration accuracy and thus the navigational accuracy of the navigation system, and how different registration methods might affect them. In this research, the registration accuracy of the optical navigation system is investigated by using a head phantom whose coordinate values of holes in the surface are measured during the navigation after different registration scenarios. Reference points are determined using computed tomography images taken from the head phantom. The absolute differences of the measured points to the corresponding reference points are calculated and the results are illustrated using bar graphs and three-dimensional point clouds. MATLAB is used to analyze and present the results. Results show that registration accuracy and thus also navigation accuracy are primarily affected by how the first three registration points are determined for the navigation system at the beginning of the registration. This should be considered in future applications where the navigation system is used in image-guided surgery.Kuvaohjatun kirurgian optisen navigointilaitteen rekisteröintitarkkuus. Tiivistelmä. Viimeisten vuosikymmenien aikana kuvaohjattu kirurgia on yleistynyt laajalti lääketieteellisten toimenpiteiden aikana ja se tarjoaa entistä paremman mahdollisuuden tarkkaan ja luotettavaan hoitoon. Kuvaohjatussa kirurgiassa navigointilaitteisto seuraa käytetyn instrumentin paikkaa ja orientaatiota operaation aikana. Navigointilaitteistoilla on erilaisia toimintaperiaatteita, joiden perusteella ne seuraavat instrumenttia. Yleisimmin käytetyt navigointilaitteistot perustuvat optiseen, mekaaniseen, tai sähkömagneettiseen seurantaan. Yleensä kuvaohjattua kirurgiaa käytetään pään ja selkärangan alueen kirurgisissa operaatioissa, joten on erittäin tärkeää, että navigointilaitteiston rekisteröinti- ja siten myös navigointitarkkuus tunnetaan, sekä erilaisten rekisteröintitapojen mahdolliset vaikutukset kyseisiin tarkkuuksiin. Tässä tutkimuksessa optisen navigointilaitteen rekisteröintitarkkuutta tutkitaan päämallin avulla, jonka pintaan luotujen reikien koordinaattiarvot mitataan navigointitilanteessa erilaisten rekisteröintitapojen jälkeen. Referenssipisteet kyseisille mittauspisteille määritetään päämallin tietokonetomografiakuvista. Mitattujen pisteiden, sekä vastaavien referenssipisteiden väliset absoluuttiset erot lasketaan ja tulokset esitetään palkkikuvaajien, sekä kolmiulotteisten pistepilvien avulla käyttäen apuna MATLAB-ohjelmistoa. Tulokset osoittavat, että rekisteröintitarkkuuteen ja siten navigointitarkkuuteen vaikuttaa eniten rekisteröintitilanteen alussa määritettävien kolmen ensimmäisen rekisteröintipisteen sijainti ja tämä tuleekin ottaa huomioon jatkossa tilanteissa, joissa navigointilaitetta käytetään kuvaohjatussa kirurgiassa

End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods