scholarly journals Nonparametric construction of probability maps under local stationarity

2017 ◽  
Vol 28 (3) ◽  
pp. e2438 ◽  
Author(s):  
Pilar García-Soidán ◽  
Raquel Menezes
Keyword(s):  
Probability Maps ◽  
Local Stationarity

scholarly journals The FLOod Probability Interpolation Tool (FLOPIT): A Simple Tool to Improve Spatial Flood Probability Quantification and Communication

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 666
Author(s):  
Mahkameh Zarekarizi ◽  
K. Joel Roop-Eckart ◽  
Sanjib Sharma ◽  
Klaus Keller
Keyword(s):  
Flood Risk ◽  
Water Surface ◽  
The United States ◽  
Surface Elevation ◽  
Exceedance Probability ◽  
Water Surface Elevation ◽  
Inundation Maps ◽  
Probability Maps ◽  
Flood Probability ◽  
Floodplain Development

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

scholarly journals Comparing Glioblastoma Surgery Decisions Between Teams Using Brain Maps of Tumor Locations, Biopsies, and Resections

2019 ◽  
pp. 1-12 ◽  
Author(s):  
Domenique M.J. Müller ◽  
Pierre A.J.T. Robe ◽  
Roelant S. Eijgelaar ◽  
Marnix G. Witte ◽  
Martin Visser ◽  
...  
Keyword(s):  
Tumor Location ◽  
Three Dimensions ◽  
Multidisciplinary Teams ◽  
Surgical Removal ◽  
Treatment Variation ◽  
Probability Maps ◽  
Resected Patient ◽  
The Right ◽  
Referral Bias ◽  
The Brain

Purpose The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity, which depends on the location within the brain. A standard to compare these decisions is lacking. We present a volumetric voxel-wise method for direct comparison between two multidisciplinary teams of glioblastoma surgery decisions throughout the brain. Methods Adults undergoing first-time glioblastoma surgery from 2012 to 2013 performed by two neuro-oncologic teams were included. Patients had had a diagnostic biopsy or resection. Preoperative tumors and postoperative residues were segmented on magnetic resonance imaging in three dimensions and registered to standard brain space. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to compare patient referral bias, indication variation, and treatment variation. To evaluate the quality of care, subgroups of differentially resected brain regions were analyzed for survival and functional outcome. Results One team included 101 patients, and the other included 174; 91 tumors were biopsied, and 181 were resected. Patient characteristics were largely comparable between teams. Distributions of tumor locations were dissimilar, suggesting referral bias. Distributions of biopsies were similar, suggesting absence of indication variation. Differentially resected regions were identified in the anterior limb of the right internal capsule and the right caudate nucleus, indicating treatment variation. Patients with (n = 12) and without (n = 6) surgical removal in these regions had similar overall survival and similar permanent neurologic deficits. Conclusion Probability maps of tumor location, biopsy, and resection provide additional information that can inform surgical decision making across multidisciplinary teams for patients with glioblastoma.

Three Graphic Representations to Aid Bayesian Inference

1988 ◽  
Vol 27 (03) ◽  
pp. 125-132 ◽  
Author(s):  
W. G. Cole
Keyword(s):  
Laboratory Test ◽  
Signal Detection ◽  
Bayesian Reasoning ◽  
False Negatives ◽  
Probability Maps ◽  
Test Parameters ◽  
Graphic Representations ◽  
Laboratory Results ◽  
Formal Properties ◽  
Sensitivity Specificity

SummaryVisual representation may help physicians and patients interpret laboratory results, for example by aiding Bayesian reasoning. This paper is concerned with the psychological and formal properties of such visual representations. One popular way to present laboratory results is via signal detection curves. These curves represent many parameters of a laboratory test including parameters, such as distribution variance, that are not typically known. Such curves can be seriously misleading.Two alternative representations are suggested. Probability maps represent only the three laboratory test parameters most likely to be known: sensitivity, specificity and prevalence, and thus avoid the problems of the richer signal detection curves. Probability maps, however, do not remind the user of why there are false positives and false negatives nor of the nature of the criterion for positivity. Detection bars, a third type of representation, are a compromise between signal detection curves and probability maps.

scholarly journals Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Carlos A. Felgueiras ◽  
Jussara O. Ortiz ◽  
Eduardo C. G. Camargo ◽  
Laércio M. Namikawa ◽  
Thales S. Körting
Keyword(s):  
Spatial Data ◽  
Three Dimensional ◽  
Real Data ◽  
Geographic Region ◽  
Geostatistical Modeling ◽  
Probability Maps ◽  
Uncertainty Model ◽  
Probability Criterion ◽  
Sample Points

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

OC-0303 Using spatial probability maps to highlight potential inaccuracies in deep learning based contours

2021 ◽  
Vol 161 ◽  
pp. S210-S211
Author(s):  
W. Verbakel ◽  
W. van Rooij ◽  
B. Slotman ◽  
M. Dahele
Keyword(s):  
Deep Learning ◽  
Spatial Probability ◽  
Probability Maps

Probability Maps of Landslide Reactivation Derived from Tree-Ring Records

2013 ◽  
pp. 409-416
Author(s):  
Jérôme Lopez Saez ◽  
Christophe Corona ◽  
Frédéric Berger
Keyword(s):  
Tree Ring ◽  
Probability Maps ◽  
Landslide Reactivation

On the cutting edge of glioblastoma surgery: where neurosurgeons agree and disagree on surgical decisions

2021 ◽  
pp. 1-11
Author(s):  
Domenique M. J. Müller ◽  
Pierre A. Robe ◽  
Hilko Ardon ◽  
Frederik Barkhof ◽  
Lorenzo Bello ◽  
...  
Keyword(s):  
Decision Making ◽  
Extent Of Resection ◽  
Brain Regions ◽  
Tumor Location ◽  
Patient Treatment ◽  
Study Cohort ◽  
Surgical Decision ◽  
Probability Maps ◽  
The Right ◽  
Surgical Decision Making

OBJECTIVE The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma. METHODS The study included all adult patients who underwent first-time glioblastoma surgery in 2012–2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival. RESULTS The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions. CONCLUSIONS Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma.

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