Curtis Lisle | University of Central Florida (original) (raw)

Papers by Curtis Lisle

bioRxiv (Cold Spring Harbor Laboratory), Dec 6, 2023

Members of different species often engage in aggressive contests over resources. This series of a... more Members of different species often engage in aggressive contests over resources. This series of aggressive contests between species may result in an interspecific dominance hierarchy. Such hierarchies are of interest because they could be used to address a variety of research questions, for example, do similarly ranked species tend to avoid each other in time or space, and what will happen when such species come into contact as climates change? Here, we propose a method for creating a continental-scale hierarchy, and we make initial analyses based on this hierarchy. Leveraging the existing network of citizen scientists from Project FeederWatch, we collected the data with which to create a continent-spanning interspecific dominance hierarchy that included species that do not currently have overlapping geographic distributions. We quantified the extent of intransitivities (rock-paper-scissors relationships) in the hierarchy, as intransitivities can promote local species’ coexistence. Overall, the hierarchy was nearly linear, and largely predicted by body mass, although there were clade-specific deviations from the average mass–dominance relationship. Warblers and orioles, for instance, were more dominant than expected based on their body mass, while buntings, grosbeaks, and doves were less dominant than expected. Intransitive relationships were rare. Few interactions were reported between close relatives and ecological competitors like Mountain and Black-capped Chickadees, as such species often have only marginally overlapping geographic distributions, restricting opportunity for observation. Yet, these species’ ranks—emergent properties of the network—were often in agreement with targeted studies of dominance relationships between them

Supplemental Table S1. Whole slide image tissue segmentation statistics by an expert pathologist ... more Supplemental Table S1. Whole slide image tissue segmentation statistics by an expert pathologist and probability prediction using a trained convolutional neural network. Supplemental Table S2. Clinical and molecular characteristics of FN-RMS samples used for training models for mutation prediction. Yellow boxes indicate genes included in defining the RAS pathway. Supplemental Table S3. Clinical information with COG risk stratification of FN-RMS samples used for training a prognostication predictive CNN.

supplementary table legend

Supplemental Description of the Rhabdomyosarcoma Web-based Application

Abstract This chapter is designed to help the reader become familiar with standard methods for de... more Abstract This chapter is designed to help the reader become familiar with standard methods for describing pelvic organ geometry, suitable for biomechanical computational analysis. To perform structural engineering analysis of the pelvic floor tissues, they must first be represented geometrically. This is done via a technique called segmentation, in which individual organs and/or tissue elements are outlined, and given unique labels. The interesting tissues are usually identified on standard radiologic image stacks — usually magnetic resonance imaging (MRI) or computed tomography (CT) scans. The goal of segmentation is to identify a desired tissue on the original (grayscale) image, and trace out (or label) its boundaries. For each organ or tissue of interest, the labeling is performed, either manually or semiautomatically. Because pelvic floor structures exist in a three-dimensional (3-D) space, it is necessary to represent the tissues as 3-D structures. This 3-D representation is facilitated by the method of acquiring MRI and CT image data. Both CT and MRI will produce grayscale source image data sets. Each data set consists of a “stack” of 2-D images, covering the region of the body that was scanned. The extent of the scanned region is called the field of view, and the grayscale images are sometimes referred to as the source images. This chapter will discuss the geometric representation of pelvic floor MRI grayscale data, as well as the segmentation of this data to produce labelmaps suitable for computational analysis and 3-D reconstruction of the organs and tissues of interest. Manual, semiautomatic, and automatic segmentation methods will be briefly introduced, in preparation for a more detailed discussion of automated segmentation in a later chapter. The chapter will conclude with a review of the trade-offs between the segmentation methods, and discussion of the issues with repeatability, reliability, and fidelity of these methods. The chapter focuses on MRI source data, with the understanding that the general principles will apply to CT sourced images as well.

Clinical Cancer Research, Nov 8, 2022

Translational Andrology and Urology, Dec 1, 2016

ObjectiveBiomechanical simulation requires accurate representation of the geometry of the structu... more ObjectiveBiomechanical simulation requires accurate representation of the geometry of the structures to be studied. Specifically, when simulating the interaction of implanted prosthetics with surrounding tissues, the geometric and mechanical properties of the prosthesis and the surrounding tissues need to be adequately represented. The present work describes methods for inserting test prostheses into magnetic resonance imaging (MRI) derived geometric models of the pelvic floor structures, in order to create realistic simulation models.MethodsWe modified an existing public domain image analysis software tool to allow placement and segmentation of arbitrarily shaped 3D objects into the output segmented geometry of a pelvic MRI image dataset. The tool was applied to create composite segmented geometry and 3D models of the MRI derived pelvic floor structures with the inserted prostheses in the intended anatomic locations, suitable for biomechanical simulation model creation.ResultsSegmentations of the organs in the source pelvic MRI datasets were created, showing the segmented embedded prostheses in the planned location. Three dimensional reconstructions of the segmented datasets were generated, which were viewable from multiple angles, and the ability to turn on and off all tissue and prosthesis layers was demonstrated.ConclusionsWe created a software application for inserting prostheses into segmented MRI based datasets. The output segmentations were suitable for input into a soft-tissue simulation tool suite, which generated simulation results suitable for analysis. This tool has the potential to enable patient specific, iterative surgical planning of prolapse repair strategies.Funding Source(s)None

Journal of pathology informatics, 2022

Background Mouse models are highly effective for studying the pathophysiology of lung adenocarcin... more Background Mouse models are highly effective for studying the pathophysiology of lung adenocarcinoma and evaluating new treatment strategies. Treatment efficacy is primarily determined by the total tumor burden measured on excised tumor specimens. The measurement process is time-consuming and prone to human errors. To address this issue, we developed a novel deep learning model to segment lung tumor foci on digitally scanned hematoxylin and eosin (H&E) histology slides. Methods Digital slides of 239 mice from 9 experimental cohorts were split into training (n=137), validation (n=37), and testing cohorts (n=65). Image patches of 500×500 pixels were extracted from 5× and 10× magnifications, along with binary masks of expert annotations representing ground-truth tumor regions. Deep learning models utilizing DeepLabV3+ and UNet architectures were trained for binary segmentation of tumor foci under varying stain normalization conditions. The performance of algorithm segmentation was assessed by Dice Coefficient, and detection was evaluated by sensitivity and positive-predictive value (PPV). Results The best model on patch-based validation was DeepLabV3+ using a Resnet-50 backbone, which achieved Dice 0.890 and 0.873 on validation and testing cohort, respectively. This result corresponded to 91.3 Sensitivity and 51.0 PPV in the validation cohort and 93.7 Sensitivity and 51.4 PPV in the testing cohort. False positives could be reduced 10-fold with thresholding artificial intelligence (AI) predicted output by area, without negative impact on Dice Coefficient. Evaluation at various stain normalization strategies did not demonstrate improvement from the baseline model. Conclusions A robust AI-based algorithm for detecting and segmenting lung tumor foci in the pre-clinical mouse models was developed. The output of this algorithm is compatible with open-source software that researchers commonly use.

Simulation, 1994

While many are familiar with flight simulators, there is also a growing body of ground-based simu... more While many are familiar with flight simulators, there is also a growing body of ground-based simulation training systems. The Army/DARPA sponsored SIMNET project (Nelms 1988) involved over 200 armor and aircraft simulators in a complex network, designed to teach combined arms combat skills. The follow-on Close Combat Tactical Trainer (CCTT) project will be the largest training simulator acquisition in history. In addition, realtime interactive simula tion is moving beyond military training into the potentially much larger market of commercial, entertainment and educational applications currently being called "Virtual Reality" (Furness 1988). However, no existing realtime simulation supports a truly interactive world. In particular, the terrain (soil, water and vegetation) is nearly or completely immutable in today's simula tors. In a word, the terrain is not dynamic. This Project explores the hypothesis that it is economically feasible to construct networked realtime simulators which incorporate useful simulations of dynamic terrain phenomena. The authors have evaticated the computational requirements of realtime graphical dynamic terrain simula tion with both theoretical models and prototypes, and conclude that useful levels of terrain dynamics can be incorporated in the next generation of low-cost, high-volume training simulators and virtual environ ments.

bioRxiv (Cold Spring Harbor Laboratory), Dec 6, 2023

Members of different species often engage in aggressive contests over resources. This series of a... more Members of different species often engage in aggressive contests over resources. This series of aggressive contests between species may result in an interspecific dominance hierarchy. Such hierarchies are of interest because they could be used to address a variety of research questions, for example, do similarly ranked species tend to avoid each other in time or space, and what will happen when such species come into contact as climates change? Here, we propose a method for creating a continental-scale hierarchy, and we make initial analyses based on this hierarchy. Leveraging the existing network of citizen scientists from Project FeederWatch, we collected the data with which to create a continent-spanning interspecific dominance hierarchy that included species that do not currently have overlapping geographic distributions. We quantified the extent of intransitivities (rock-paper-scissors relationships) in the hierarchy, as intransitivities can promote local species’ coexistence. Overall, the hierarchy was nearly linear, and largely predicted by body mass, although there were clade-specific deviations from the average mass–dominance relationship. Warblers and orioles, for instance, were more dominant than expected based on their body mass, while buntings, grosbeaks, and doves were less dominant than expected. Intransitive relationships were rare. Few interactions were reported between close relatives and ecological competitors like Mountain and Black-capped Chickadees, as such species often have only marginally overlapping geographic distributions, restricting opportunity for observation. Yet, these species’ ranks—emergent properties of the network—were often in agreement with targeted studies of dominance relationships between them

Supplemental Table S1. Whole slide image tissue segmentation statistics by an expert pathologist ... more Supplemental Table S1. Whole slide image tissue segmentation statistics by an expert pathologist and probability prediction using a trained convolutional neural network. Supplemental Table S2. Clinical and molecular characteristics of FN-RMS samples used for training models for mutation prediction. Yellow boxes indicate genes included in defining the RAS pathway. Supplemental Table S3. Clinical information with COG risk stratification of FN-RMS samples used for training a prognostication predictive CNN.

supplementary table legend

Supplemental Description of the Rhabdomyosarcoma Web-based Application

Abstract This chapter is designed to help the reader become familiar with standard methods for de... more Abstract This chapter is designed to help the reader become familiar with standard methods for describing pelvic organ geometry, suitable for biomechanical computational analysis. To perform structural engineering analysis of the pelvic floor tissues, they must first be represented geometrically. This is done via a technique called segmentation, in which individual organs and/or tissue elements are outlined, and given unique labels. The interesting tissues are usually identified on standard radiologic image stacks — usually magnetic resonance imaging (MRI) or computed tomography (CT) scans. The goal of segmentation is to identify a desired tissue on the original (grayscale) image, and trace out (or label) its boundaries. For each organ or tissue of interest, the labeling is performed, either manually or semiautomatically. Because pelvic floor structures exist in a three-dimensional (3-D) space, it is necessary to represent the tissues as 3-D structures. This 3-D representation is facilitated by the method of acquiring MRI and CT image data. Both CT and MRI will produce grayscale source image data sets. Each data set consists of a “stack” of 2-D images, covering the region of the body that was scanned. The extent of the scanned region is called the field of view, and the grayscale images are sometimes referred to as the source images. This chapter will discuss the geometric representation of pelvic floor MRI grayscale data, as well as the segmentation of this data to produce labelmaps suitable for computational analysis and 3-D reconstruction of the organs and tissues of interest. Manual, semiautomatic, and automatic segmentation methods will be briefly introduced, in preparation for a more detailed discussion of automated segmentation in a later chapter. The chapter will conclude with a review of the trade-offs between the segmentation methods, and discussion of the issues with repeatability, reliability, and fidelity of these methods. The chapter focuses on MRI source data, with the understanding that the general principles will apply to CT sourced images as well.

Clinical Cancer Research, Nov 8, 2022

Translational Andrology and Urology, Dec 1, 2016

ObjectiveBiomechanical simulation requires accurate representation of the geometry of the structu... more ObjectiveBiomechanical simulation requires accurate representation of the geometry of the structures to be studied. Specifically, when simulating the interaction of implanted prosthetics with surrounding tissues, the geometric and mechanical properties of the prosthesis and the surrounding tissues need to be adequately represented. The present work describes methods for inserting test prostheses into magnetic resonance imaging (MRI) derived geometric models of the pelvic floor structures, in order to create realistic simulation models.MethodsWe modified an existing public domain image analysis software tool to allow placement and segmentation of arbitrarily shaped 3D objects into the output segmented geometry of a pelvic MRI image dataset. The tool was applied to create composite segmented geometry and 3D models of the MRI derived pelvic floor structures with the inserted prostheses in the intended anatomic locations, suitable for biomechanical simulation model creation.ResultsSegmentations of the organs in the source pelvic MRI datasets were created, showing the segmented embedded prostheses in the planned location. Three dimensional reconstructions of the segmented datasets were generated, which were viewable from multiple angles, and the ability to turn on and off all tissue and prosthesis layers was demonstrated.ConclusionsWe created a software application for inserting prostheses into segmented MRI based datasets. The output segmentations were suitable for input into a soft-tissue simulation tool suite, which generated simulation results suitable for analysis. This tool has the potential to enable patient specific, iterative surgical planning of prolapse repair strategies.Funding Source(s)None

Journal of pathology informatics, 2022

Background Mouse models are highly effective for studying the pathophysiology of lung adenocarcin... more Background Mouse models are highly effective for studying the pathophysiology of lung adenocarcinoma and evaluating new treatment strategies. Treatment efficacy is primarily determined by the total tumor burden measured on excised tumor specimens. The measurement process is time-consuming and prone to human errors. To address this issue, we developed a novel deep learning model to segment lung tumor foci on digitally scanned hematoxylin and eosin (H&E) histology slides. Methods Digital slides of 239 mice from 9 experimental cohorts were split into training (n=137), validation (n=37), and testing cohorts (n=65). Image patches of 500×500 pixels were extracted from 5× and 10× magnifications, along with binary masks of expert annotations representing ground-truth tumor regions. Deep learning models utilizing DeepLabV3+ and UNet architectures were trained for binary segmentation of tumor foci under varying stain normalization conditions. The performance of algorithm segmentation was assessed by Dice Coefficient, and detection was evaluated by sensitivity and positive-predictive value (PPV). Results The best model on patch-based validation was DeepLabV3+ using a Resnet-50 backbone, which achieved Dice 0.890 and 0.873 on validation and testing cohort, respectively. This result corresponded to 91.3 Sensitivity and 51.0 PPV in the validation cohort and 93.7 Sensitivity and 51.4 PPV in the testing cohort. False positives could be reduced 10-fold with thresholding artificial intelligence (AI) predicted output by area, without negative impact on Dice Coefficient. Evaluation at various stain normalization strategies did not demonstrate improvement from the baseline model. Conclusions A robust AI-based algorithm for detecting and segmenting lung tumor foci in the pre-clinical mouse models was developed. The output of this algorithm is compatible with open-source software that researchers commonly use.

Simulation, 1994

While many are familiar with flight simulators, there is also a growing body of ground-based simu... more While many are familiar with flight simulators, there is also a growing body of ground-based simulation training systems. The Army/DARPA sponsored SIMNET project (Nelms 1988) involved over 200 armor and aircraft simulators in a complex network, designed to teach combined arms combat skills. The follow-on Close Combat Tactical Trainer (CCTT) project will be the largest training simulator acquisition in history. In addition, realtime interactive simula tion is moving beyond military training into the potentially much larger market of commercial, entertainment and educational applications currently being called "Virtual Reality" (Furness 1988). However, no existing realtime simulation supports a truly interactive world. In particular, the terrain (soil, water and vegetation) is nearly or completely immutable in today's simula tors. In a word, the terrain is not dynamic. This Project explores the hypothesis that it is economically feasible to construct networked realtime simulators which incorporate useful simulations of dynamic terrain phenomena. The authors have evaticated the computational requirements of realtime graphical dynamic terrain simula tion with both theoretical models and prototypes, and conclude that useful levels of terrain dynamics can be incorporated in the next generation of low-cost, high-volume training simulators and virtual environ ments.