Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS-telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat-use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat-use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat-use studiespublishedVersio

An open electromagnetic tracking framework applied to targeted liver tumour ablation

Purpose: Electromagnetic tracking is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-line-of-sight environments, allowing instrument navigation in locations where optical tracking is not feasible. EMT can be beneficial in applications such as percutaneous radiofrequency ablation for the treatment of hepatic lesions where the needle tip may be obscured due to difficult liver environments (e.g subcutaneous fat or ablation artefacts). Advances in the field of EMT include novel methods of improving tracking system accuracy, precision and error compensation capabilities, though such system-level improvements cannot be readily incorporated in current therapy applications due to the ‘blackbox’ nature of commercial tracking solving algorithms. Methods: This paper defines a software framework to allow novel EMT designs, and improvements become part of the global design process for image-guided interventions. An exemplary framework is implemented in the Python programming language and demonstrated with the open-source Anser EMT system. The framework is applied in the preclinical setting though targeted liver ablation therapy on an animal model. Results: The developed framework was tested with the Anser EMT electromagnetic tracking platform. Liver tumour targeting was performed using the tracking framework with the CustusX navigation platform using commercially available electromagnetically tracked needles. Ablation of two tumours was performed with a commercially available ablation system. Necropsy of the tumours indicated ablations within 5 mm of the tumours. Conclusions: An open-source framework for electromagnetic tracking was presented and effectively demonstrated in the preclinical setting. We believe that this framework provides a structure for future advancement in EMT system in and customised instrument design

Laboratory test of Single Landmark registration method for ultrasound-based navigation in laparoscopy using an open-source platform

Purpose Test the feasibility of the novel Single Landmark image-to-patient registration method for use in the operating room for future clinical trials. The algorithm is implemented in the open-source platform CustusX, a computer-aided intervention research platform dedicated to intraoperative navigation and ultrasound, with an interface for laparoscopic ultrasound probes. Methods The Single Landmark method is compared to fiducial landmark on an IOUSFAN (Kyoto Kagaku Co., Ltd., Japan) soft tissue abdominal phantom and T2 magnetic resonance scans of it. Results The experiments show that the accuracy of the Single Landmark registration is good close to the registered point, increasing with the distance from this point (12.4 mm error at 60 mm away from the registered point). In this point, the registration accuracy is mainly dominated by the accuracy of the user when clicking on the ultrasound image. In the presented set-up, the time required to perform the Single Landmark registration is 40% less than for the FLRM. Conclusion The Single Landmark registration is suitable for being integrated in a laparoscopic workflow. The statistical analysis shows robustness against translational displacements of the patient and improvements in terms of time. The proposed method allows the clinician to accurately register lesions intraoperatively by clicking on these in the ultrasound image provided by the ultrasound transducer. The Single Landmark registration method can be further combined with other more accurate registration approaches improving the registration at relevant points defined by the clinicians

Laboratory test of Single Landmark registration method for ultrasound-based navigation in laparoscopy using an open-source platform

Purpose Test the feasibility of the novel Single Landmark image-to-patient registration method for use in the operating room for future clinical trials. The algorithm is implemented in the open-source platform CustusX, a computer-aided intervention research platform dedicated to intraoperative navigation and ultrasound, with an interface for laparoscopic ultrasound probes. Methods The Single Landmark method is compared to fiducial landmark on an IOUSFAN (Kyoto Kagaku Co., Ltd., Japan) soft tissue abdominal phantom and T2 magnetic resonance scans of it. Results The experiments show that the accuracy of the Single Landmark registration is good close to the registered point, increasing with the distance from this point (12.4 mm error at 60 mm away from the registered point). In this point, the registration accuracy is mainly dominated by the accuracy of the user when clicking on the ultrasound image. In the presented set-up, the time required to perform the Single Landmark registration is 40% less than for the FLRM. Conclusion The Single Landmark registration is suitable for being integrated in a laparoscopic workflow. The statistical analysis shows robustness against translational displacements of the patient and improvements in terms of time. The proposed method allows the clinician to accurately register lesions intraoperatively by clicking on these in the ultrasound image provided by the ultrasound transducer. The Single Landmark registration method can be further combined with other more accurate registration approaches improving the registration at relevant points defined by the clinicians.publishedVersion© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/

Using the CustusX toolkit to create an image guided bronchoscopy application: Fraxinus

Purpose The aim of this paper is to show how a specialized planning and guidance application called Fraxinus, can be built on top of the CustusX platform (www.custusx.org), which is an open source image-guided intervention software platform. Fraxinus has been customized to meet the clinical needs in navigated bronchoscopy. Methods The application requirements for Fraxinus were defined in close collaboration between research scientists, software developers and clinicians (pulmonologists), and built on top of CustusX. Its superbuild system downloads specific versions of the required libraries and builds them for the application in question, including the selected plugins. New functionality is easily added through the plugin framework. The build process enables the creation of specialized applications, adding additional documentation and custom configurations. The toolkit’s libraries offer building blocks for image-guided applications. An iterative development process was applied, where the clinicians would test and provide feedback during the entire process. Results Fraxinus has been developed and is released as an open source planning and guidance application built on top of CustusX. It is highly specialized for bronchoscopy. The proposed workflow is adapted to the different steps in this procedure. The user interface of CustusX has been modified to enhance information, quality assurance and user friendliness with the intention to increase the overall yield for the patient. As the workflow of the procedure is relatively constant, some actions are predicted and automatically performed by the application, according to the requirements from the clinicians. Conclusions The CustusX platform facilitates development of new and specialized applications. The toolkit supports the process and makes important extension and injection points available for customization.publishedVersio

3D ultrasound reconstruction algorithms from analog and digital data

acceptedVersio

Using the CustusX toolkit to create an image guided bronchoscopy application: Fraxinus

Purpose The aim of this paper is to show how a specialized planning and guidance application called Fraxinus, can be built on top of the CustusX platform (www.custusx.org), which is an open source image-guided intervention software platform. Fraxinus has been customized to meet the clinical needs in navigated bronchoscopy. Methods The application requirements for Fraxinus were defined in close collaboration between research scientists, software developers and clinicians (pulmonologists), and built on top of CustusX. Its superbuild system downloads specific versions of the required libraries and builds them for the application in question, including the selected plugins. New functionality is easily added through the plugin framework. The build process enables the creation of specialized applications, adding additional documentation and custom configurations. The toolkit’s libraries offer building blocks for image-guided applications. An iterative development process was applied, where the clinicians would test and provide feedback during the entire process. Results Fraxinus has been developed and is released as an open source planning and guidance application built on top of CustusX. It is highly specialized for bronchoscopy. The proposed workflow is adapted to the different steps in this procedure. The user interface of CustusX has been modified to enhance information, quality assurance and user friendliness with the intention to increase the overall yield for the patient. As the workflow of the procedure is relatively constant, some actions are predicted and automatically performed by the application, according to the requirements from the clinicians. Conclusions The CustusX platform facilitates development of new and specialized applications. The toolkit supports the process and makes important extension and injection points available for customization

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS-telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat-use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat-use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat-use studies. citizen science, habitat selection, opportunistic data, preferential sampling, spatial bias, ungulate

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS-telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat-use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat-use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat-use studie