Segmentation of the mandibular canal in cone-beam CT data

Accurate information about the location of the mandibular canal is essential in case of dental implant surgery. The goal of our research is to find an automatic method which can segment the mandibular canal in Cone-beam CT (CBCT). \ud Mandibular canal segmentation methods in literature using a priori shape information are, the 2D active appearance model of Rueda et al., and 3D active shape model (ASM) of Kainmueller et al. The mean distance to manual annotation of the mandibular canal of the method of Kainmueller is around 1.1mm. The best method in literature is Kim et al. with an average distance of 0.7mm.\ud We develop and evaluate five methods for mandibular canal localization. The methods, Lukas Kanade tracking (LK), B-spline registration, demon registration, 3D active shape model (ASM), and active appearance model (AAM). The ASM and AAM need corresponding points between the mandibles in the training data. We develop and evaluate two methods to find corresponding points, minimum description length (MDL) and the second shape context (SC) based registration. To improve the quality of the CBCT scans we introduce a rotational invariant edge preserving optimized anisotropic diffusion filter.\ud We evaluate the performance on 13 CBCT scans. The registration methods have an average distance to expert annotation of the canal of more than 4mm, LK tracking a distance of 3mm, AAM and ASM a distance of respectively 2.0mm and 2.3mm. The MDL method does not improve point correspondences found by the SC method, and the pre-filtering with the introduced diffusion filter does not improve the ASM result. By using location based intensity weights we improve the AAM results, to an average distance of 1.88mm. The relatively large error is due to a low number of training datasets, and low CBCT scan quality

Optimized Anisotropic Rotational Invariant Diffusion Scheme on Cone-Beam CT

Cone-beam computed tomography (CBCT) is an important image modality for dental surgery planning, with high resolution images at a relative low radiation dose. In these scans the mandibular canal is hardly visible, this is a problem for implant surgery planning. We use anisotropic diffusion filtering to remove noise and enhance the mandibular canal in CBCT scans. For the diffusion tensor we use hybrid diffusion with a continuous switch (HDCS), suitable for filtering both tubular as planar image structures. We focus in this paper on the diffusion discretization schemes. The standard scheme shows good isotropic filtering behavior but is not rotational invariant, the diffusion scheme of Weickert is rotational invariant but suffers from checkerboard artifacts. We introduce a new scheme, in which we numerically optimize the image derivatives. This scheme is rotational invariant and shows good isotropic filtering properties on both synthetic as real CBCT data

Learning in migration management? Persistent side effects of the EUTF

acceptedVersionPeer reviewe

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Coherence Filtering to Enhance the Mandibular Canal in Cone-Beam CT data

Segmenting the mandibular canal from cone beam CT data, is difficult due to low edge contrast and high image noise. We introduce 3D coherence filtering as a method to close the interrupted edges and denoise the structure of the mandibular canal. Coherence Filtering is an anisotropic non-linear tensor based diffusion algorithm for edge enhancing image filtering. We test different numerical schemes of the tensor diffusion equation, non-negative, standard discretization and also a rotation invariant scheme of Weickert [1]. Only the scheme of Weickert did not blur the high spherical images frequencies on the image diagonals of our test volume. Thus this scheme is chosen to enhance the small curved mandibular canal structure. The best choice of the diffusion equation parameters c1 and c2, depends on the image noise. Coherence filtering on the CBCT-scan works well, the noise in the mandibular canal is gone and the edges are connected. Because the algorithm is tensor based it cannot deal with edge joints or splits, thus is less fit for more complex image structures

Multimodal image registration by edge attraction and regularization using a B-spline grid

Multi modal image registration enables images from different modalities to be analyzed in the same coordinate system. The class of B-spline-based methods that maximize the Mutual Information between images produce satisfactory result in general, but are often complex and can converge slowly. The popular Demons algorithm, while being fast and easy to implement, produces unrealistic deformation fields and is sensitive to illumination differences between the two images, which makes it unsuitable for multi-modal registration in its original form. We propose a registration algorithm that combines a B-spline grid with deformations driven by image forces. The algorithm is easy to implement and is robust against large differences in the appearance between the images to register. The deformation is driven by attraction-forces between the edges in both images, and a B-spline grid is used to regularize the sparse deformation field. The grid is updated using an original approach by weighting the deformation forces for each pixel individually with the edge strengths. This approach makes the algorithm perform well even if not all corresponding edges are present. We report preliminary results by applying the proposed algorithm to a set of (multi-modal) test images. The results show that the proposed method performs well, but is less accurate than state of the art registration methods based on Mutual Information. In addition, the algorithm is used to register test images to manually drawn line images in order to demonstrate the algorithm's robustness

Evaluation of the potential of automatic segmentation of the mandibular canal using cone-beam computed tomography

We aimed to investigate the effectiveness of software for automatically tracing the mandibular canal on data from cone-beam computed tomography (CT). After the data had been collected from one dentate and one edentate fresh cadaver head, both a trained Active Shape Model (ASM) and an Active Appearance Model (AAM) were used to automatically segment the canals from the mandibular to the mental foramen. Semiautomatic segmentation was also evaluated by providing the models with manual annotations of the foramina. To find out if the tracings were in accordance with the actual anatomy, we compared the position of the automatic mandibular canal segmentations, as displayed on cross-sectional cone-beam CT views, with histological sections of exactly the same region. The significance of differences between results were analysed with the help of Fisher's exact test and Pearson's correlation coefficient. When tracings based on AAM and ASM were used, differences between cone-beam CT and histological measurements varied up to 3.45 mm and 4.44 mm, respectively. Manual marking of the mandibular and mental foramina did not improve the results, and there were no significant differences (p = 0.097) among the methods. The accuracy of automatic segmentation of the mandibular canal by the AAM and ASM methods is inadequate for use in clinical practice

Semi-automatic deformable registration of prostate MR images to pathological slices

Purpose: To present a semi-automatic deformable registration algorithm for co-registering T2-weighted (T2w) images of the prostate with whole-mount pathological sections of prostatectomy specimens. - \ud Materials and Methods: Twenty-four patients underwent 1.5 Tesla (T) endorectal MR imaging before radical prostatectomy with whole-mount step-section pathologic analysis of surgical specimens. For each patient, the T2w imaging containing the largest area of tumor was manually matched with the corresponding pathologic slice. The prostate was co-registered using a free-form deformation (FFD) algorithm based on B-splines. Registration quality was assessed through differences between prostate diameters measured in right–left (RL) and anteroposterior (AP) directions on T2w images and pathologic slices and calculation of the Dice similarity coefficient, D, for the whole prostate (WP), the peripheral zone (PZ) and the transition zone (TZ). - \ud Results: The mean differences in diameters measured on pathology and MR imaging in the RL direction and the AP direction were 0.49 cm and −0.63 cm, respectively, before registration and 0.10 cm and −0.11 cm, respectively, after registration. The mean D values for the WP, PZ and TZ, were 0.76, 0.65, and 0.77, respectively, before registration and increased to 0.91, 0.76, and 0.85, respectively, after registration. The improvements in D were significant for all three tissues (P < 0.001 for all). - \ud Conclusion: The proposed semi-automatic method enabled successful co-registration of anatomical prostate MR images to pathologic slices