Open-ST: High-resolution spatial transcriptomics in 3D.

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

Acto3D: an open-source user-friendly volume rendering software for high-resolution 3D fluorescence imaging in biology.

Advances in fluorescence microscopy and tissue-clearing have revolutionised 3D imaging of fluorescently labelled tissues, organs and embryos. However, the complexity and high cost of existing software and computing solutions limit their widespread adoption, especially by researchers with limited resources. Here, we present Acto3D, an open-source software, designed to streamline the generation and analysis of high-resolution 3D images of targets labelled with multiple fluorescent probes. Acto3D provides an intuitive interface for easy 3D data import and visualisation. Although Acto3D offers straightforward 3D viewing, it performs all computations explicitly, giving users detailed control over the displayed images. Leveraging an integrated graphics processing unit, Acto3D deploys all pixel data to system memory, reducing visualisation latency. This approach facilitates accurate image reconstruction and efficient data processing in 3D, eliminating the need for expensive high-performance computers and dedicated graphics processing units. We have also introduced a method for efficiently extracting lumen structures in 3D. We have validated Acto3D by imaging mouse embryonic structures and by performing 3D reconstruction of pharyngeal arch arteries while preserving fluorescence information. Acto3D is a cost-effective and efficient platform for biological research.

Estimating conflict losses and reporting biases.

Determining the number of casualties and fatalities suffered in militarized conflicts is important for conflict measurement, forecasting, and accountability. However, given the nature of conflict, reliable statistics on casualties are rare. Countries or political actors involved in conflicts have incentives to hide or manipulate these numbers, while third parties might not have access to reliable information. For example, in the ongoing militarized conflict between Russia and Ukraine, estimates of the magnitude of losses vary wildly, sometimes across orders of magnitude. In this paper, we offer an approach for measuring casualties and fatalities given multiple reporting sources and, at the same time, accounting for the biases of those sources. We construct a dataset of 4,609 reports of military and civilian losses by both sides. We then develop a statistical model to better estimate losses for both sides given these reports. Our model accounts for different kinds of reporting biases, structural correlations between loss types, and integrates loss reports at different temporal scales. Our daily and cumulative estimates provide evidence that Russia has lost more personnel than has Ukraine and also likely suffers from a higher fatality to casualty ratio. We find that both sides likely overestimate the personnel losses suffered by their opponent and that Russian sources underestimate their own losses of personnel.

Versatile and automated workflow for the analysis of oligodendroglial calcium signals.

Although intracellular Ca2+ signals of oligodendroglia, the myelin-forming cells of the central nervous system, regulate vital cellular processes including myelination, few studies on oligodendroglia Ca2+ signal dynamics have been carried out and existing software solutions are not adapted to the analysis of the complex Ca2+ signal characteristics of these cells. Here, we provide a comprehensive solution to analyze oligodendroglia Ca2+ imaging data at the population and single-cell levels. We describe a new analytical pipeline containing two free, open source and cross-platform software programs, Occam and post-prOccam, that enable the fully automated analysis of one- and two-photon Ca2+ imaging datasets from oligodendroglia obtained by either ex vivo or in vivo Ca2+ imaging techniques. Easily configurable, our software solution is optimized to obtain unbiased results from large datasets acquired with different imaging techniques. Compared to other recent software, our solution proved to be fast, low memory demanding and faithful in the analysis of oligodendroglial Ca2+ signals in all tested imaging conditions. Our versatile and accessible Ca2+ imaging data analysis tool will facilitate the elucidation of Ca2+-mediated mechanisms in oligodendroglia. Its configurability should also ensure its suitability with new use cases such as other glial cell types or even cells outside the CNS.

Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves.

BACKGROUND: Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. RESULTS: Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters. CONCLUSIONS: Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

OmicNavigator: open-source software for the exploration, visualization, and archival of omic studies.

BACKGROUND: The results of high-throughput biology ('omic') experiments provide insight into biological mechanisms but can be challenging to explore, archive and share. The scale of these challenges continues to grow as omic research volume expands and multiple analytical technologies, bioinformatic pipelines, and visualization preferences have emerged. Multiple software applications exist that support omic study exploration and/or archival. However, an opportunity remains for open-source software that can archive and present the results of omic analyses with broad accommodation of study-specific analytical approaches and visualizations with useful exploration features. RESULTS: We present OmicNavigator, an R package for the archival, visualization and interactive exploration of omic studies. OmicNavigator enables bioinformaticians to create web applications that interactively display their custom visualizations and analysis results linked with app-derived analytical tools, graphics, and tables. Studies created with OmicNavigator can be viewed within an interactive R session or hosted on a server for shared access. CONCLUSIONS: OmicNavigator can be found at https://github.com/abbvie-external/OmicNavigator.

GIANI - open-source software for automated analysis of 3D microscopy images.

The study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a limited number of open-source software packages to analyse such images. Here, we describe General Image Analysis of Nuclei-based Images (GIANI; https://djpbarry.github.io/Giani), new software for the analysis of 3D images. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images, and comes with scripting and command line tools. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in confocal images of mouse early embryos and by segmenting nuclei from light-sheet microscopy images of the flour beetle embryo. We also validate the performance of the software using simulated data. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.

Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.

Quantification of follicles in human ovarian tissue using image processing software and trained artificial intelligence†.

Cancer survival rates in prepubertal girls and young women have risen in recent decades due to increasingly efficient treatments. However, many such treatments are gonadotoxic, causing premature ovarian insufficiency, loss of fertility, and ovarian endocrine function. Implantation of donor ovarian tissue encapsulated in immune-isolating capsules is a promising method to restore physiological endocrine function without immunosuppression or risk of reintroducing cancer cells harbored by the tissue. The success of this approach is largely determined by follicle density in the implanted ovarian tissue, which is analyzed manually from histologic sections and necessitates specialized, time-consuming labor. To address this limitation, we developed a fully automated method to quantify follicle density that does not require additional coding. We first analyzed ovarian tissue from 12 human donors between 16 and 37 years old using semi-automated image processing with manual follicle annotation and then trained artificial intelligence program based on follicle identification and object classification. One operator manually analyzed 102 whole slide images from serial histologic sections. Of those, 77 images were assessed by a second manual operator, followed with an automated method utilizing artificial intelligence. Of the 1181 follicles the control operator counted, the comparison operator counted 1178, and the artificial intelligence counted 927 follicles with 80% of those being correctly identified as follicles. The three-stage artificial intelligence pipeline finished 33% faster than manual annotation. Collectively, this report supports the use of artificial intelligence and automation to select tissue donors and grafts with the greatest follicle density to ensure graft longevity for premature ovarian insufficiency treatment.

Replication of the bSTAR sequence and open-source implementation.

PURPOSE: The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half-radial dual-echo imaging technique (bSTAR) using open-source frameworks as a response to the 2023 ISMRM "repeat it with me" Challenge. METHODS: We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open-source rapid sequence prototyping environment. Image reconstruction is performed with the open-source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open-source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open-source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers. RESULTS: Both echo images obtained from open-source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head-to-head comparison between open-source bSTAR and original bSTAR on a healthy volunteer indicates that open-source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR. CONCLUSION: We have successfully replicated bSTAR lung imaging at 0.55T using two open-source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.

Repeatability of Open-MOLLI: An open-source inversion recovery myocardial T1 mapping sequence for fast prototyping.

PURPOSE: To develop an open-source prototype of myocardial T1 mapping (Open-MOLLI) to improve accessibility to cardiac T1 mapping and evaluate its repeatability. With Open-MOLLI, we aim to enable faster implementation and testing of sequence modifications and to facilitate inter-scanner and cross-vendor reproducibility studies. METHODS: Open-MOLLI is an inversion-recovery sequence using a balanced SSFP (bSSFP) readout, with inversion and triggering schemes based on the 5(3)3 MOLLI sequence, developed in Pulseq. Open-MOLLI and MOLLI sequences were acquired in the ISMRM/NIST phantom and 21 healthy volunteers. In 18 of those subjects, Open-MOLLI and MOLLI were repeated in the same session (test-retest). RESULTS: Phantom T1 values were comparable between methods, specifically for the vial with reference T1 value most similar to healthy myocardium T1 (T1vial3 = 1027 ms): T1MOLLI = 1011 ± 24 ms versus T1Open-MOLLI = 1009 ± 20 ms. In vivo T1 estimates were similar between Open-MOLLI and MOLLI (T1MOLLI = 1004 ± 33 ms vs. T1Open-MOLLI = 998 ± 52 ms), with a mean difference of -17 ms (p = 0.20), despite noisier Open-MOLLI weighted images and maps. Repeatability measures were slightly higher for Open-MOLLI (RCMOLLI = 3.0% vs. RCOpen-MOLLI = 4.4%). CONCLUSION: The open-source sequence Open-MOLLI can be used for T1 mapping in vivo with similar mean T1 values to the MOLLI method. Open-MOLLI increases the accessibility to cardiac T1 mapping, providing also a base sequence to which further improvements can easily be added and tested.

CMRsim-A python package for cardiovascular MR simulations incorporating complex motion and flow.

PURPOSE: To present an open-source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction. METHODS: CMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase-contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided. RESULTS: The Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk-motion phase sensitivity took approximately 10 s per diffusion-weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain. CONCLUSION: CMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open-source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI).

PURPOSE: Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS: A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS: The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS: The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI.

PURPOSE: To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. METHODS: We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). RESULTS: Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. CONCLUSION: The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Exploring pathways toward open-hardware ecosystems to safeguard genetic resources for biomedical research communities using aquatic model species.

Development of reliable germplasm repositories is critical for preservation of genetic resources of aquatic species, which are widely utilized to support biomedical innovation by providing a foundational source for naturally occurring variation and development of new variants through genetic manipulations. A significant barrier in repository development is the lack of cryopreservation capability and reproducibility across the research community, posing great risks of losing advances developed from billions of dollars of research investment. The emergence of open scientific hardware has fueled a new movement across biomedical research communities. With the increasing accessibility of consumer-level fabrication technologies, such as three-dimensional printers, open hardware devices can be custom designed, and design files distributed to community members for enhancing rigor, reproducibility, and standardization. The overall goal of this review is to explore pathways to create open-hardware ecosystems among the communities using aquatic model resources for biomedical research. To gain feedback and insights from community members, an interactive workshop focusing on open-hardware applications in germplasm repository development was held at the 2022 Aquatic Models for Human Disease Conference, Woods Hole, Massachusetts. This work integrates conceptual strategies with practical insights derived from workshop interactions using examples of germplasm repository development. These insights can be generalized for establishment of open-hardware ecosystems for a broad biomedical research community. The specific objectives were to: (1) introduce an open-hardware ecosystem concept to support biomedical research; (2) explore pathways toward open-hardware ecosystems through four major areas, and (3) identify opportunities and future directions.

Expanding access to magnetic resonance education through open-source web tutorials.

This study presents a tool that introduces the fundamental concepts of magnetic resonance (MR) by integrating related science, technology, engineering, arts, and mathematical (STEAM) topics in the form of games to improve the access to MR education.

A European regulatory pathway for Tidepool loop following clearance in the United States?

The recent clearance by the United States Food and Drug Administration of Tidepool Loop sets an important precedent within the medical device landscape. For the first time, an automated insulin delivery mobile application-based on an algorithm initially designed and developed by users -has been recognised as safe and effective by a regulatory body. The aim of this paper is twofold: firstly, we map out the regulatory pathways and processes that were navigated by Tidepool, the non-profit behind Tidepool Loop, in order to make this landmark moment possible. Secondly, we set out potential approvals processes in the European Union and United Kingdom with a view to examining the challenges to obtaining regulatory clearance for Tidepool Loop in these jurisdictions. In so doing, we highlight the significant differences, not only between the United States and European systems but also between the European Union and Great Britain systems. We conclude by arguing that the complexity encountered when seeking to introduce an innovative solution in different regulatory systems has the potential to act as a disincentive to open source developers from seeking regulatory approvals for such technologies in the future.

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM).

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images. Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy. We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies.

Development and construction of an open-platform, open-source low-cost portable uroflowmeter: The OpenFlow device.

BACKGROUND: Uroflowmetry is useful to screen for and manage many voiding disorders. Home-based uroflowmetry might better represent the patient's true voiding pattern and be more widely adopted if an accurate low-cost portable device was available. OBJECTIVE: Development and initial evaluation of an open-platform, open-source low-cost portable uroflowmeter. MATERIALS AND METHODS: We designed and built an uroflowmeter comprising of a load cell and digital memory card unit connected to a programmable microcontroller board mounted upon a 3D printed frame. It generated date-stamped tables which were processed and plotted. Twenty urologists were recruited to assess the device. Each participant received the equipment that was returned, along with a bladder diary, after at least 24 consecutive hours of homemade uroflowmetry recording. Additionally, were assessed with the International Prostatic Symptom Score (I-PSS) and Peeling diagram, whereas the device's ease of use, robustness, and portability were evaluated with a Likert-type questionnaire. Two experienced urodynamicists independently evaluated the tracings' quality rated with a 3° ordinal scale: (1) Interpretable without artifacts; (2) Interpretable with artifacts; (3) Uninterpretable. RESULTS: Participants' median age was 36.6 years old, none having an I-PSS > 5 or Peeling > 2. Overall 138 voidings were recorded (77 daytime, 61 nightly episodes). The device's ease of use, robustness, and portability obtained maximum score in 80% of evaluations. Most (98%) of the tracings were considered interpretable. Limitations included its small study population and short monitoring times. CONCLUSION: The construction of a cheap (<50 USD), accurate user-friendly portable uroflowmeter proved feasible, which could facilitate access to portable uroflowmetry.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.