Autonomous Trajectory Planning for External Ventricular Drain Placement

Abstract BACKGROUND External ventricular drain (EVD) placement is the most frequently performed neurosurgical procedure for management of various conditions including hydrocephalus, traumatic brain injury, and stroke. State-of-the-art computational pattern recognition techniques could improve the safety and accuracy of EVD placement. Placement of the Kocher's point EVD is the most common neurosurgical procedure which is often performed in urgent conditions. OBJECTIVE To present the development of a novel computer algorithm identifying appropriate anatomy and autonomously plan EVD placement on clinical computed tomography (CT) scans. METHODS The algorithm was tested on 2 data sets containing 5-mm slice noncontrast CT scans. The first contained images of 300 patients without significant intracranial pathology (normal), the second of 43 patients with significant acute intracranial hemorrhage. Automated planning was performed by custom 2-tiered heuristic with run-time template selection in combination with refinement using nonlinear image registration. RESULTS Automated EVD planning was accurate in 297 of 300 normal and 41 of 43 patient cases. In the normal data set, mean distance between Kocher's point and the ipsilateral foramen of Monro was 63 ± 3.1 mm in women and 65 ± 6.5 mm in men (P = .0008). Trajectory angle with respect to the sagittal plane was 91 ± 6° in women and 90 ± 6° in men (obtuse posterior) (P = .15); to the coronal plane, 85 ± 6° and 86 ± 5° in women and men (P = .12), respectively (acute lateral). CONCLUSION A combination of linear and nonlinear image registration techniques accurately planned EVD trajectory in 99% of normal scans and 95% of scans with significant intracranial hemorrhage.

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Impact of baseline clinical and radiological features on outcome of chronic rhinosinusitis in granulomatosis with polyangiitis

10.21203/rs.3.rs-67859/v2 ◽

2020 ◽

Author(s):

Sigrun Skaar Holme ◽

Karin Kilian ◽

Heidi B. Eggesbø ◽

Jon Magnus Moen ◽

Øyvind Molberg

Keyword(s):

Chronic Rhinosinusitis ◽

Granulomatosis With Polyangiitis ◽

Negative Impact ◽

Renal Involvement ◽

Ct Scans ◽

Data Sets ◽

Data Set ◽

Mucosal Disease ◽

Life Threatening ◽

Observation Period

Abstract Background: Granulomatosis with polyangiitis (GPA) causes a recurring inflammation in nose and paranasal sinuses that clinically resembles chronic rhinosinusitis (CRS) of other aetiologies. While sinonasal inflammation is not among the life-threatening features of GPA, patients report it to have major negative impact on quality of life. A relatively large proportion of GPA patients have severe CRS with extensive damage to nose and sinus structures evident by CT, but risk factors for severe CRS development remain largely unknown. In this study, we aimed to identify clinical and radiological predictors of CRS-related damage in GPA.Methods: We included GPA patients who had clinical data sets from time of diagnosis, and two or more paranasal sinus CT scans obtained ≥ 12 months apart available for analysis. We defined time from first to last CT as the study observation period, and evaluated CRS development across this period using CT scores for inflammatory sinus bone thickening (osteitis), bone destructions and sinus opacifications (here defined as mucosal disease). In logistic regression, we applied osteitis as main outcome measure for CRS-related damage.Results: We evaluated 697 CT scans obtained over median 5 years observation from 116 GPA patients. We found that 39% (45/116) of the GPA patients remained free from CRS damage across the study observation period, while 33% (38/116) had progressive damage. By end of observation, 32% (37/116) of the GPA patients had developed severe osteitis. We identified mucosal disease at baseline as a predictor for osteitis (Odds Ratio 1.33), and we found that renal involvement at baseline was less common in patients with severe osteitis at last CT (41%, 15/37) than in patients with no osteitis (60%, 27/45).Conclusions: In this largely unselected GPA patient cohort, baseline sinus mucosal disease associated with CRS-related damage, as measured by osteitis at end of follow-up. We found no significant association with clinical factors, but the data set indicated an inverse relationship between renal involvement and severe sinonasal affliction.

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Brain Extraction Using Active Contour Neighborhood-Based Graph Cuts Model

Symmetry ◽

10.3390/sym12040559 ◽

2020 ◽

Vol 12 (4) ◽

pp. 559 ◽

Cited By ~ 1

Author(s):

Shaofeng Jiang ◽

Yu Wang ◽

Xuxin Zhou ◽

Zhen Chen ◽

Suhua Yang

Keyword(s):

Local Convergence ◽

Active Contour ◽

Sagittal Plane ◽

Brain Mri ◽

Graph Cuts ◽

Dice Similarity Coefficient ◽

Data Sets ◽

Similarity Coefficients ◽

Data Set ◽

Brain Extraction

The extraction of brain tissue from brain MRI images is an important pre-procedure for the neuroimaging analyses. The brain is bilaterally symmetric both in coronal plane and transverse plane, but is usually asymmetric in sagittal plane. To address the over-smoothness, boundary leakage, local convergence and asymmetry problems in many popular methods, we developed a brain extraction method using an active contour neighborhood-based graph cuts model. The method defined a new asymmetric assignment of edge weights in graph cuts for brain MRI images. The new graph cuts model was performed iteratively in the neighborhood of brain boundary named the active contour neighborhood (ACN), and was effective to eliminate boundary leakage and avoid local convergence. The method was compared with other popular methods on the Internet Brain Segmentation Repository (IBSR) and OASIS data sets. In testing cross IBSR data set (18 scans with 1.5 mm thickness), IBSR data set (20 scans with 3.1 mm thickness) and OASIS data set (77 scans with 1 mm thickness), the mean Dice similarity coefficients obtained by the proposed method were 0.957 ± 0.013, 0.960 ± 0.009 and 0.936 ± 0.018 respectively. The result obtained by the proposed method is very similar with manual segmentation and achieved the best mean Dice similarity coefficient on IBSR data. Our experiments indicate that the proposed method can provide competitively accurate results and may obtain brain tissues with sharp brain boundary from brain MRI images.

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Frameless neuronavigation with computer vision and real-time tracking for bedside external ventricular drain placement: a cadaveric study

Journal of Neurosurgery ◽

10.3171/2021.5.jns211033 ◽

2021 ◽

pp. 1-10

Author(s):

Faith C. Robertson ◽

Raahil M. Sha ◽

Jose M. Amich ◽

Walid Ibn Essayed ◽

Avinash Lal ◽

...

Keyword(s):

Computer Vision ◽

Image Registration ◽

Real Time ◽

Tracking System ◽

External Ventricular Drain ◽

Surface Registration ◽

Ct Scans ◽

Registration System ◽

Registration Error ◽

Real Time Tracking

OBJECTIVE A major obstacle to improving bedside neurosurgical procedure safety and accuracy with image guidance technologies is the lack of a rapidly deployable, real-time registration and tracking system for a moving patient. This deficiency explains the persistence of freehand placement of external ventricular drains, which has an inherent risk of inaccurate positioning, multiple passes, tract hemorrhage, and injury to adjacent brain parenchyma. Here, the authors introduce and validate a novel image registration and real-time tracking system for frameless stereotactic neuronavigation and catheter placement in the nonimmobilized patient. METHODS Computer vision technology was used to develop an algorithm that performed near-continuous, automatic, and marker-less image registration. The program fuses a subject’s preprocedure CT scans to live 3D camera images (Snap-Surface), and patient movement is incorporated by artificial intelligence–driven recalibration (Real-Track). The surface registration error (SRE) and target registration error (TRE) were calculated for 5 cadaveric heads that underwent serial movements (fast and slow velocity roll, pitch, and yaw motions) and several test conditions, such as surgical draping with limited anatomical exposure and differential subject lighting. Six catheters were placed in each cadaveric head (30 total placements) with a simulated sterile technique. Postprocedure CT scans allowed comparison of planned and actual catheter positions for user error calculation. RESULTS Registration was successful for all 5 cadaveric specimens, with an overall mean (± standard deviation) SRE of 0.429 ± 0.108 mm for the catheter placements. Accuracy of TRE was maintained under 1.2 mm throughout specimen movements of low and high velocities of roll, pitch, and yaw, with the slowest recalibration time of 0.23 seconds. There were no statistically significant differences in SRE when the specimens were draped or fully undraped (p = 0.336). Performing registration in a bright versus a dimly lit environment had no statistically significant effect on SRE (p = 0.742 and 0.859, respectively). For the catheter placements, mean TRE was 0.862 ± 0.322 mm and mean user error (difference between target and actual catheter tip) was 1.674 ± 1.195 mm. CONCLUSIONS This computer vision–based registration system provided real-time tracking of cadaveric heads with a recalibration time of less than one-quarter of a second with submillimetric accuracy and enabled catheter placements with millimetric accuracy. Using this approach to guide bedside ventriculostomy could reduce complications, improve safety, and be extrapolated to other frameless stereotactic applications in awake, nonimmobilized patients.

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Development and Validation of Patient-Specific Finite Element Models of the Hemipelvis Generated From a Sparse CT Data Set

Journal of Biomechanical Engineering ◽

10.1115/1.2960368 ◽

2008 ◽

Vol 130 (5) ◽

Cited By ~ 32

Author(s):

Vickie B. Shim ◽

Rocco P. Pitto ◽

Robert M. Streicher ◽

Peter J. Hunter ◽

Iain A. Anderson

Keyword(s):

Finite Element ◽

Material Properties ◽

Patient Data ◽

Ct Scans ◽

Patient Specific ◽

Data Sets ◽

Fe Model ◽

Data Set ◽

Spatially Varying ◽

Ct Slices

To produce a patient-specific finite element (FE) model of a bone such as the pelvis, a complete computer tomographic (CT) or magnetic resonance imaging (MRI) geometric data set is desirable. However, most patient data are limited to a specific region of interest such as the acetabulum. We have overcome this problem by providing a hybrid method that is capable of generating accurate FE models from sparse patient data sets. In this paper, we have validated our technique with mechanical experiments. Three cadaveric embalmed pelves were strain gauged and used in mechanical experiments. FE models were generated from the CT scans of the pelves. Material properties for cancellous bone were obtained from the CT scans and assigned to the FE mesh using a spatially varying field embedded inside the mesh while other materials used in the model were obtained from the literature. Although our FE meshes have large elements, the spatially varying field allowed them to have location dependent inhomogeneous material properties. For each pelvis, five different FE meshes with a varying number of patient CT slices (8–12) were generated to determine how many patient CT slices are needed for good accuracy. All five mesh types showed good agreement between the model and experimental strains. Meshes generated with incomplete data sets showed very similar stress distributions to those obtained from the FE mesh generated with complete data sets. Our modeling approach provides an important step in advancing the application of FE models from the research environment to the clinical setting.

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Impact of Baseline Clinical and Radiological Features on Outcome of Chronic Rhinosinusitis in Granulomatosis with Polyangiitis

10.21203/rs.3.rs-67859/v1 ◽

2020 ◽

Author(s):

Sigrun Skaar Holme ◽

Karin Kilian ◽

Heidi B. Eggesbø ◽

Jon Magnus Moen ◽

Øyvind Molberg

Keyword(s):

Chronic Rhinosinusitis ◽

Granulomatosis With Polyangiitis ◽

Negative Impact ◽

Renal Involvement ◽

Ct Scans ◽

Data Sets ◽

Data Set ◽

Mucosal Disease ◽

Life Threatening ◽

Observation Period

Abstract Background: Granulomatosis with polyangiitis (GPA) causes a recurring inflammation in nose and paranasal sinuses that clinically resembles chronic rhinosinusitis (CRS) of other aetiologies. While sinonasal inflammation is not among the life-threatening features of GPA, patients report it to have major negative impact on quality of life. A relatively large proportion of GPA patients have severe CRS with extensive damage to nose and sinus structures evident by CT, but risk factors for severe CRS development remain largely unknown. In this study, we aimed to identify clinical and radiological predictors of CRS-related damage in GPA. Methods: We included GPA patients who had clinical data sets from time of diagnosis, and two or more paranasal sinus CT scans obtained ≥ 12 months apart available for analysis. We defined time from first to last CT as the study observation period, and evaluated CRS development across this period by CT scores for inflammatory sinus bone thickening (osteitis), bone destructions and sinus opacifications (here defined as mucosal disease). In logistic regression, we applied osteitis as main outcome measure for CRS-related damage.Results: We evaluated 697 CT scans obtained over median 5 years observation from 116 GPA patients. We found that 39% (45/116) of the GPA patients remained free from CRS damage across the study observation period, while 33% (38/116) had progressive damage. By end of observation, 32% (37/116) of the GPA patients had developed severe osteitis. We identified mucosal disease at baseline as a predictor for osteitis (Odds Ratio 1.34), and we found that renal involvement at baseline was less common in patients with severe osteitis at last CT (41%, 15/37) than in patients with no osteitis (60%, 27/45). Conclusions: In this largely unselected GPA patient cohort, baseline sinus mucosal disease associated with CRS-related damage, as measured by osteitis at end of follow-up. We found no significant association with clinical factors, but the data set indicated an inverse relationship between renal involvement and severe sinonasal affliction.

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The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

The Entomologist s monthly magazine ◽

10.31184/m00138908.1542.3906 ◽

2018 ◽

Vol 154 (2) ◽

pp. 149-155

Author(s):

Michael Archer

Keyword(s):

Population Dynamics ◽

Population Dynamic ◽

Ecological Factors ◽

Social Wasp ◽

Data Sets ◽

Data Set ◽

Vespula Germanica ◽

The Social ◽

Minimum Number ◽

Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

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Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207321666180212162028 ◽

2018 ◽

Vol 21 (2) ◽

pp. 117-124 ◽

Cited By ~ 4

Author(s):

Bakhtyar Sepehri ◽

Nematollah Omidikia ◽

Mohsen Kompany-Zareh ◽

Raouf Ghavami

Keyword(s):

Variable Selection ◽

Predictive Power ◽

Selection Method ◽

Data Sets ◽

Data Set ◽

Comfa Model ◽

Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

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Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327908666180727123657 ◽

2019 ◽

Vol 9 (1) ◽

pp. 16-31

Author(s):

Kyungkoo Jun

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Short Term Memory ◽

Window Size ◽

Sensor Data ◽

Data Sets ◽

Data Set ◽

Proposed Model ◽

Testing Data ◽

Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

Applied Spectroscopy ◽

10.1177/0003702819839098 ◽

2019 ◽

Vol 73 (8) ◽

pp. 893-901

Author(s):

Sinead J. Barton ◽

Bryan M. Hennelly

Keyword(s):

Cosmic Ray ◽

Data Sets ◽

Biological Cells ◽

Statistical Classification ◽

Signal To Noise ◽

Multivariate Statistical ◽

Data Set ◽

Artefact Removal ◽

Single Capture ◽

Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

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Imbalanced Data Detection Kernel Method in Closed Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3652 ◽

2013 ◽

Vol 756-759 ◽

pp. 3652-3658

Author(s):

You Li Lu ◽

Jun Luo

Keyword(s):

Kernel Methods ◽

Kernel Method ◽

Imbalanced Data ◽

Data Detection ◽

Data Sets ◽

System Call ◽

Data Set ◽

Imbalanced Data Sets ◽

Lower Complexity ◽

Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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