Semi-automatic Method Research Articles

Left Atrial Wall Thickness Measured by a Machine Learning Method Predicts AF Recurrence After Pulmonary Vein Isolation.

Left atrial (LA) remodeling plays a significant role in the progression of atrial fibrillation (AF). Although LA wall thickness (LAWT) has emerged as an indicator of structural remodeling, its impact on AF outcomes remains unclear. We aimed to determine the association between LAWT and AF recurrence after pulmonary vein isolation (PVI), as well as to evaluate the relationship between LAWT and LA fibrosis. Single-center registry of patients enrolled for radiofrequency PVI from 2016 to 2018. In all cases, a pre-ablation CT was performed within less than 48 h. Mean LAWT was retrospectively measured by a semi-automated machine learning method (ADAS 3D). A subgroup of patients also underwent pre-ablation cardiac MRI. The primary endpoint was time to AF recurrence after a 3-month blanking period. A total of 439 patients (mean age 61 ± 12 years, 62% male, 78% with paroxysmal AF) were included. The mean LAWT was 1.4 ± 0.2 mm (0.9-1.9 mm). During a median follow-up of 5.8 (IQR: 4.9-6.6) years, 238 patients (54%) had an AF relapse. After adjusting for 8 clinical and imaging potential confounders, LAWT remained an independent predictor of time-to-recurrence (aHR: 4.25 [95% CI: 1.65-10.95], p = 0.003). AF recurrence rates were 11%, 15%, and 21%/year across terciles of increasing LAWT (log-rank p < 0.001). Additionally, the AF recurrence rate increased across the spectrum of LA structural remodeling, ranging from 8% (normal LAWT and LAVI) to 30%/year (LAWT and LAVI both increased). In the 62 patients who also underwent pre-ablation MRI, a moderate relationship between LAWT and fibrosis (assessed by late-gadolinium enhancement) was found (Spearman R 0.468; p < 0.001). Mean LAWT, easily assessed by commercially available machine learning software, is an independent predictor of time to AF recurrence after PVI in the long term. Whether patients with increased LAWT should receive tailored therapy deserves further investigation.

Semiautomated measures of foveal immaturity from handheld swept-source optical coherence tomography are associated with retinopathy of prematurity severity

Background/aimsHandheld swept-source optical coherence tomography (SS-OCT) was previously used to measure foveal maturity through semiautomated methods in awake premature infants. This study assesses the relationship between foveal maturity and retinopathy...

Measurements of neurite extension and nucleokinesis in an iPSC-derived model system following microtubule perturbation.

In neurons, patterns of different microtubule types are essential for neurite extension and nucleokinesis. Cellular model systems such as rodent primary cultures and induced pluripotent stem cells (iPSC)-derived neurons have provided key insights into how these patterns are created and maintained through the action of microtubule-associated proteins (MAPs), motor proteins, and regulatory enzymes. iPSC-derived models show tremendous promise but lack benchmarking and validation relative to rodent primary cultures. Here we have characterized a recent iPSC-derived model, in which doxycycline-induced expression of Neurogenin-2 drives consistent trans-differentiation into the neuronal state (EBiSC-NEUR1 neurons, referred to as NGN2 neurons below). We developed a suite of open-access, semi-automated methods to measure neurite extension and nucleokinesis of NGN2 neurons, which compare favorably to published data from other models. Then, we challenged NGN2 neurons with a panel of drugs that perturb microtubule physiology. NGN2 neurons extension and nucleokinesis were significantly perturbed by two microtubule-targeting drugs, namely a taxane (paclitaxel) and a vinca alkaloid (DZ-2384). In contrast, inhibition of microtubule severing (spastazoline) or of deacetylation (trichostatin A) had a limited effect on nucleokinesis only. Our results support the primary importance of microtubule dynamics in neuronal development and demonstrate the power of NGN2 neurons as a model system. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Automated Characterization of Intrastromal Corneal Cuts Induced by Two Femtosecond Laser Systems Using OCT Imaging

Optical coherence tomography (OCT) has gained momentum on segmenting anterior corneal substructures, such as treatment-induced flaps and lenticule cuts created by femtosecond lasers. However, recent semi-automated methods rely on manual markers, which can introduce bias and variability. In this work, we present an automated algorithm designed to overcome the limitations in the geometric quantification of intrastromal cuts produced by two different femtosecond lasers, using a unique imaging system. Our analysis, conducted on peri-operative segmentations of goat eyes, aims to demonstrate the method’s potential for contributing to ongoing efforts and enhancing clinical outcomes in refractive surgery treatment.

Semi-Automated rock block volume extraction from high-resolution 3D point clouds for enhanced rockfall hazard analysis

Rockfalls are critical landslide phenomena affecting human activities, with risk assessment based on hazard evaluation and potential impacts on exposed elements. Traditional methods for estimating unstable rock block volumes require direct measures often in hard-to-reach areas, hazardous, with time consuming approaches. This study introduces a semi-automatic method for estimating the most probable volume of the unstable blocks using open-source software (CloudCompare) to process 3D Point Cloud (PC) data obtained via Terrestrial Laser Scanning (TLS). The application area is a rock slope in a coastal sector of the northern Sicily (Italy) affected by frequent rockfalls phenomena. Both traditional field surveys and TLS were employed to characterize discontinuities and perform kinematic analyses. Volumes of already fallen blocks were directly measured, while unstable blocks were identified and volumetrically assessed using the PC-based procedure. Statistical analysis revealed that both created datasets conform to lognormal distributions; direct measurements show a better fit due to a larger sample size. Moreover, direct and indirect approaches were applied for recognition of main discontinuity sets influencing block detachment through planar sliding, toppling, and wedge failure. The proposed method offers a safer, more efficient alternative for rock mass characterization. Integration of traditional and remote sensing techniques facilitates accurate hazard evaluation, enhancing risk reduction strategies in vulnerable areas.

Mapping Seagrass Distribution and Abundance: Comparing Areal Cover and Biomass Estimates Between Space-Based and Airborne Imagery

This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km2 of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which each pixel was classified as seagrass. Seagrass patches encompassing at least nine pixels (~200 m2) were almost always detected by our classification algorithm. Using an OF threshold equal to or greater than &gt;60% provided a high level of confidence in seagrass presence while effectively reducing the impact of small misclassifications, often of individual pixels, that appeared sporadically in individual images. The image-to-image uncertainty in seagrass retrieval from the satellite images was 0.1 km2 or 2.3%, reflecting the robustness of our classification method and allowing confidence in the accuracy of the seagrass area estimate. The satellite-retrieved leaf area index (LAI) was consistent with previous in situ measurements, leading to the estimate that 2700 tons of carbon per year are produced by the Santa Rosa Sound seagrass ecosystem, equivalent to a drawdown of approximately 10,070 tons of CO2. This satellite-based approach offers a cost-effective, semi-automated, and scalable method of assessing the distribution and abundance of submerged aquatic vegetation that provides numerous ecosystem services.

Comparison of Deep Learning approaches in classification of lacial landforms

Glacial landforms, created by the continuous movements of glaciers over millennia, are crucial topics in geomorphological research. Their systematic analysis affords invaluable insights into past climatic oscillations and augments understanding of long-term climate change dynamics. The classification of these types of terrain traditionally depends on labor-intensive manual or semi-automated methods. However, the emergence of automated techniques driven by deep learning and neural networks holds promise for enhancing efficiency of terrain classification workflows. This study evaluated the effectiveness of Convolutional Neural Network (CNN) architectures, particularly Residual Neural Network (ResNet) and VGG in comparison with Vision Transformer (ViT) architecture in the glacial landform classification task. By using preprocessed input data from Digital Elevation Model (DEM) which covers regions such as the Lubawa Upland and Gardno-Leba Plain in Poland, as well as the Elise Glacier in Svalbard, Norway, comprehensive assessments of those methods were conducted. The final results highlight the unique ability of deep learning methods to accurately classify glacial landforms. Classification process presented in this study can be the efficient, repeatable and fast solution for automatic terrain classification.

Enhancing accuracy of compressed Convolutional Neural Networks through a transfer teacher and reinforcement guided training curriculum

Model compression techniques, such as network pruning, quantization and knowledge distillation, are essential for deploying large Convolutional Neural Networks (CNNs) on resource-constrained devices. Nevertheless, these techniques frequently lead to an accuracy loss, which affects performance in applications where precision is crucial. To mitigate accuracy loss, a novel method integrating Curriculum Learning (CL) with model compression, is proposed. Curriculum learning is a training approach in machine learning that involves progressively training a model on increasingly difficult samples. Existing CL approaches primarily rely on the manual design of scoring the difficulty of samples as well as pacing the easy to difficult examples for training. This gives rise to limitations such as inflexibility, need for expert domain knowledge and a decline in performance. Thereby, we propose a novel curriculum learning approach TRACE-CNN, i.e Transfer-teacher and Reinforcement-guided Adaptive Curriculum for Enhancing Convolutional Neural Networks, to address these limitations. Our semi-automated CL method consists of a pre-trained transfer teacher model whose performance serves as a measure of difficulty for the training examples. Furthermore, we employ a reinforcement learning technique to schedule training according to sample difficulty rather than establishing a fixed scheduler. Experiments on two benchmark datasets demonstrate that our method, when integrated into a model compression pipeline, effectively reduces the accuracy loss usually associated with such compression techniques.

The development of a routable pavement network dataset to support active travel

ABSTRACT Active travel (AT) networks have only recently become a priority for urban planners in many countries, and networks of spatial data and applications that use them are also lacking. Road networks have historically been used as a proxy for pedestrian networks, yet they are a poor one as human movement is much less constrained than that of motorised vehicles. This study presents a novel approach to producing an attribute-rich network for AT, together with a semi-automated method to produce a width-attributed pavement network from topographic mapping agency data (based on a case study of Ordnance Survey (OS) MasterMap data from the UK). A shortest path network routing application using Dijkstra’s algorithm is presented to demonstrate the possibilities of AT routing incorporating increased attribution. The paper concludes by firstly proposing additional steps to further enhance the attribution of the network and finally by describing the relevant policy implications of this work.

A semi-automated method using object-based image analysis (OBIA) to detect and enumerate beluga whales in summer from very high-resolution (VHR) satellite imagery.

Very high-resolution (VHR) satellite imagery has proven to be useful for detection of large to medium cetaceans, such as odontocetes and offers some significant advantages over traditional detection methods. However, the significant time investment needed to manually read satellite imagery is currently a limiting factor to use this method across large open ocean regions. The objective of this study is to develop a semi-automated detection method using object-based image analysis to identify beluga whales (Delphinapterus leucas) in open water (summer) ocean conditions in the Arctic using panchromatic WorldView-3 satellite imagery and compare the detection time between human read and algorithm detected imagery. The false negative rate, false positive rate, and automated count deviation were used to assess the accuracy and reliability of various algorithms for reading training and test imagery. The best algorithm, which used spectral mean and texture variance attributes, detected no false positives and the false negative rate was low (<4%). This algorithm was able to accurately and reliably identify all the whales detected by experienced readers in the ice-free panchromatic image. The autodetection algorithm does have difficulty separately identifying whales that are perpendicular to one another, whales below the surface, and may use multiple segments to define a whale. As a result, for determining counts of whales, a reader should manually review the automated results. However, object-based image analysis offers a viable solution for processing large amounts of satellite imagery for detecting medium-sized beluga whales while eliminating all areas of the imagery which are whale-free. This algorithm could be adapted for detecting other cetaceans in ice-free water.

Semi-automated analysis of faults with meter-scale displacements and networks of planar features using high-resolution 3D seismic to investigate gas leakage at the Vestnesa Ridge, eastern Fram Strait

We develop a semiautomated method for extracting faults and other planar features from two high-resolution (HR) seismic volumes in the eastern Fram Strait. We assess seismic data quality and resolution prior to structural interpretation to determine the appropriate processing workflow. This workflow, designed for HR data sets, identifies meter-scale (&gt;3–4 m) faults and other planar features, picked up by subtle changes in the curvature and discontinuities of seismic reflections, to investigate their relationship with fluid migration in the shallow subsurface. Although evidence exists for Holocene (approximately &lt;8 ka) fault-associated gas seepage, structures in both volumes are presently sealing. We assess structures based on those that have measurable throw (faults) and those that do not (planar features). Structures lacking measurable throw are significant because they appear to represent fluid flow pathways. This technique enhances the analysis of the geometries of the mapped structures, facilitating the interpretation of meter-scale fault throws and the orientations of planar features. We provide insights into the evolution of structures previously linked to episodes of fluid migration and gas leakage at the Vestnesa Ridge. We infer zones of dilation (i.e., opening) at locations where faults or networks of planar features interact and investigate how these structures influence localized uplift and shallowing of the interface between free gas and gas hydrate (i.e., the bottom-simulating reflection).

Geological study of the seafloor applied to marine renewable energies

ABSTRACT This paper shows an example of how to create morphological and geological maps of the seabed combining high-resolution bathymetric data and onshore geological information. The mapped area corresponds to the BIMEP offshore test site, located in the south-eastern part of the Bay of Biscay, in the northern part of the Iberian Peninsula. In the Morphological map, using semi-automatic methods, the seafloor is categorized in 10 classes, obtained by the combination of terrain variables and a decision table. In the Geological map, onshore-defined geological units are extended into the continental shelf, and bedding traces, folds, and fractures are depicted. These maps could serve as valuable tools for stakeholders involved in marine energy projects, enabling informed decision-making and promoting sustainable development practices.

TMOD-20. RAPID PROCESSING AND BIOBANKING OF RESECTED BRAIN TUMOR TISSUE TO GENERATE PATIENT-DERIVED BRAIN TUMOR EXPLANT ORGANOIDS (GBOS) FOR PRECLINICAL STUDIES

Abstract Improved preclinical tools are urgently needed to translate new brain cancer treatments. Patient-derived brain tumor explant organoids (GBOs) offer promise for studying tumor cells in a relevant human tumor microenvironment and predicting patient responses to therapy. However, generating GBOs is time-consuming, costly, and technically challenging, hindering the creation of comprehensive biobanks covering the spectrum of brain tumor heterogeneity. This work presents a semi-automated method for producing GBOs, involving tumor tissue processing, size selection, and same-day cryopreservation, reducing generation time to less than 1 hour. We established a biobank of 24 GBOs from 33 samples (11 males and 13 females) with primary (n=13, GBO Yield (GY)&amp;gt;90%) and recurrent (n=5, GY&amp;gt;90%) glioblastomas, high-grade gliomas (HGG, n=2, GY≤50%), and low-grade gliomas (LGG, n=2 with GY&amp;gt;90%, n=2 with GY≤50%). Using GBO size and propidium iodide as readouts, we used this biobank to compare anticancer activities of novel idronoxil-conjugated benzopyran compounds (NX786, NX904/904E1) against Bortezomib (100% cell death reference). NX786 and NX904/904E1 reduced GBO growth (≥50%) in all primary glioblastoma and one (of two) recurrent glioblastoma GBOs with milder effects in LGG GBOs. Two (of four) primary glioblastoma, one (of two) recurrent glioblastoma, and one LGG GBOs exhibited significant cell death (&amp;gt;60%) in response to NX904/904E1. In contrast, NX786 induced cell death in one (of three) primary glioblastoma (34% cell death) and one (of two) recurrent glioblastoma (71% cell death) GBOs but not in LGG GBOs, together showcasing varied responses across different brain tumors. Highly-passaged GBOs lacking the non-malignant tumor microenvironment were more susceptible to these treatments, underlining the tumor microenvironment’s critical role in responses to anticancer agents. We provide a new method for efficient processing and cryopreserving GBOs, enabling the establishment of large biobanks for patient-specific preclinical drug testing across brain tumor subgroups in a clinically relevant human 3D model.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Automated pericardium segmentation and epicardial adipose tissue quantification from computed tomography images

Background and ObjectiveEpicardial Adipose Tissue (EAT) is regarded as an independent risk factor for cardiovascular disease, and an increase in its volume is closely associated with disorders such as coronary artery atherosclerosis. Traditional manual and semi-automatic methods for EAT segmentation rely on subjective judgment, resulting in uncertainty and unreliability, which limits their application in clinical practice. Therefore, this study aims to develop a fully automatic segmentation and quantification method to improve the accuracy of EAT assessment. MethodsA Boundary-Enhanced Multi-scale U-Net network with a Convolutional Transformer (BMT-UNet) is developed to segment the pericardium. The BMT-UNet comprises Boundary-Enhanced (BE) modules, Multi-Scale (MS) modules, and a Convolutional Transformer (ConvT) module. The MS and BE modules in the encoding part are designed to capture detailed boundary features and accurately delineate the pericardium boundary by combining multi-scale features with morphological operations, leveraging their complementarity. The ConvT module integrates global contextual information, thereby enhancing overall segmentation accuracy and addressing the issue of internal holes in the segmented pericardial images. The volume of EAT is automatically quantified using standard fat thresholds with a range of −190 to −30 HU. ResultsFor a Coronary Computed Tomography Angiography (CCTA) dataset which contained 50 patients, the Dice coefficient and Hausdorff distance for the proposed method of pericardial and EAT segmentation are 98.3% ± 0.2%, 5.7±0.8 mm, and 93.9% ± 1.7%, 2.1 ± 0.3 mm, respectively. The linear regression coefficient between the EAT volume segmented and the actual volume is 0.982, and the Pearson correlation coefficient is 0.99. Bland-Altman analysis further confirmed the high consistency between the automated and manual methods. These results demonstrate a significant improvement over existing methods, particularly in terms of segmentation precision and reliability, which are critical for clinical application. ConclusionsThis work develops an automated method for quantifying EAT in Computed Tomography (CT) images, and the results agreed closely with expert evaluations. Code is available at: https://github.com/wy-9903/BMT-UNet.

Real-Time Leaves Segmentation in RGB Images with Deep Learning in a Single-Board Computer

Abstract. This work proposed and evaluated methods for real-time leaf segmentation using a single-board computer. The main aim was to explore the state-of-the-art techniques based on the YOLO algorithm for real-time operation. For this purpose, the available variants of YOLOv8 and YOLOv9 were evaluated, and a semi-automatic labelling method based on the Segment Anything Model (SAM) algorithm was used. Given the need to delimit the leaf contour for labelling, it was possible to create a larger and more accurate dataset compared to the purely manual procedure. In addition, the cost-benefit of the applied algorithms and methods were assessed, considering the computational demand required, as well as the accuracy, recall, and precision delivered by these techniques. In this study, both quantitative analysis of the trained architectures' metrics and qualitative examination through direct observation of images were conducted to identify crucial aspects. The experiments were conducted with a post-processed dataset and the suitability for real-time applications was based on the elapsed time for segmentation. We concluded that the YOLOv8n architecture is the best one among those tested, presenting a precision and recall of 0.9064 and 0.7233, respectively. This architecture represents the best cost-benefit ratio between computational cost and real-time performance, being able to perform segmentation in 310 ms with the NVIDIA Jetson Nano board. Furthermore, when computational cost is not a problem or even when segmentation time can be higher, the YOLO8m network may be recommended when the recall metric is more important than precision. This network presented a precision and recall of 0.8556 and 0.7726, respectively, and presented a better performance in segmenting leaves located in more complex parts of the image and with a higher recall.

A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies

Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea (Gera Gulf, Lesvos Island, Greece). The complexity of this environment makes it an ideal area to “crash test” (test to the limit) and compare the results of the delineation methods. A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. Three semi-automated tools were also employed to map the reefs: the Benthic Terrain Modeler (BTM), Confined Morphologies Mapping (CoMMa), and eCognition Multiresolution Segmentation. BTM did not function properly with irregular reef footprints, but by modifying both the bathymetry and slope, the outcome was improved, producing accurate results that appeared to exceed the accuracy of manual mapping. CoMMa, a new GIS morphometric toolbox, was a “one-stop shop” that, besides generating satisfactory reef delineation results (i.e., detecting the same total reef area as the manual method), was also used to extract the morphometric characteristics of the polygons resulting from all the methods. Lastly, the Multiresolution Segmentation also gave satisfactory results with the highest precision. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the goodness-of-fit (GOF) with the Precision, Recall, and F1 Scores producing values higher than 0.78, suggesting a good detection accuracy for the semi-automated methods. The analysis reveals that the semi-automated methods provided more efficient results in comparison with the time-consuming manual mapping. Overall, for this case study, the modification of the bathymetry and slope enabled the results’ accuracy to be further enhanced. This study asserts that the use of semi-automated mapping is an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making.

New subsurface structural insights of Northeast Vietnam: Advanced implications from high-resolution magnetic data

Northeast Vietnam is covered by sedimentary rocks, and its subsurface structures can be determined by geophysical studies. However, the structural patterns in Northeast Vietnam have so far been poorly studied and there is a lack of addressing the complex geological features of this area. Outlining such geological features is commonly achieved by applying various edge detection methods to magnetic fields. In this research, we applied nine enhancement techniques to magnetic data of Northeast Vietnam to identify geological features in this area. The methods are tested on synthetic data before applying to RTP (reduction to pole) magnetic data that are obtained from the multiple stage RTP technique. Four semiautomated depth estimation methods are also used to obtain the depth of magnetic structures in Northeast Vietnam. The results indicate that the normalized total gradient and power transform of the tilt angle of the total horizontal gradient can produce geological features more clearly than others. The findings show that the study area is dominated by NE-SW and ENE-WSW trending lineaments, and most of magnetic sources are around 1–2.5 km deep in the area. The NE-SW and ENE-WSW trending structures are possibly related to the left-lateral motion along the Ailaoshan–Red River shear zone. Our findings also show that Northeast Vietnam may be considered as a southwestern continuation of the South China Block. Our results provide a better picture of the structural features of Northeast Vietnam and overcome the gap in understanding the tectonic and geological framework of this important area.

Semiautomated Three-Dimensional Landmark Placement on Knee Models Is a Reliable Method to Describe Bone Shape and Alignment

PurposeThe purpose of this study was to assess the inter- and intra-rater reliability of 21 anatomical landmarks initially placed with an AI-algorithm and then manually verified with human input. Methods30 knee CT scans of participants from the Multicenter Osteoarthritis Study (MOST) ages 45-55 years were included. Approximately one-half experienced progression of patellofemoral osteoarthritis, defined as an increased cartilage score in the patellofemoral compartment on MRI over 2 years. The algorithm automatically placed 19 anatomic landmarks on the femur, tibia, and patella. An additional 2 landmarks were added manually. Two landmark reviewers separately reviewed all 30 scans and verified all landmarks. After 2 weeks, one reviewer repeated the process for the same dataset. The mean Euclidean distance of manual landmark displacement, mean absolute disagreement between and within raters, and intraclass correlation coefficients for inter- and intra-rater reliability were calculated. ResultsAll landmarks had excellent inter-rater reliability. The tibial and femoral shaft centers had ICCs of 1, indicating their positions did not differ. Seventeen landmarks had ICCs between 0.90-0.99 and the tibial tuberosity had an ICC of 0.87. Intra-rater reliability scores were 1 for 16 landmarks and between 0.90-0.99 for the remaining five. ConclusionThere was excellent agreement on the locations of all 21 landmarks evaluated in this study. Clinical RelevanceThe potential role of artificial intelligence in medical imaging and orthopedic research is a growing area of interest. The excellent reliability demonstrated across multiple landmarks in our study reveals the potential for semi-automated 3D methods to enhance precision of anatomical measurements of the knee over 2D methods.

Development of a Spectrophotometric Flow-Based System for the Determination of Total Polyphenol Content in Legume Flours.

Conventional nutritional characterization of legume flours comprises costly and laborious analytical methods for nutrient quantification that establish the quality of seeds, including their macronutrient quantification, fiber, mineral, antioxidant content, and more. The quantification of total polyphenol content (TPC) in legumes is performed using different analytic methods, namely the Folin-Ciocalteu (FC) method, which is lengthy and employs potentially toxic reagents. Additionally, it is time-consuming and prone to human error if carried out in a conventional batch mode. To develop a semi-automatic method for a faster, greener, and more precise TPC quantification, resorting to flow injection analysis (FIA). The development of the FIA method was based on the FC method. The flow manifold was structured by performing an array of preliminary studies, which led to the establishment of the method that displayed the highest sensitivity (highest slope of the calibration curve). The method was applied to determine the TPC in various legume flours. This novel method resulted in a throughput of 1 analysis per minute, allowing to analyze 20 samples in triplicate within an hour, with a LOD of 4.32 mg L-1, a LOQ of 14.4 mg L-1, and an RSD of 4.4%. The results calculated from the proposed FIA method agreed with those of the reference procedure. By using the FIA system, a lower consumption of reagents was observed. Additionally, as there is no need to reach chemical equilibrium, a high throughput has been achieved, resulting in a faster and greener method for the determination of polyphenols in various types of legumes.