Individual Segments Research Articles

The ISPRS International Contest on Individual Tree Crown Segmentation using High-Resolution Images and the Initial Findings

Abstract. Tree canopy plays an essential role in the biophysical activities in forest environment. During the past two decades, individual tree delineation using high-resolution imagery data has become a hot topic in forest sensing research. Individual Tree Crown (ITC) segmentation methods aim to generate masks that delineating the boundary of each ITC, which supports various tree parameter extractions. Thanks to the rapid development of deep learning, the ITC segmentation methods achieved remarkable improvement. However, existing research suffers from the limited availability of the datasets, and the lack of evaluation standards as well as task-orientated neural networks. In 2024, the International Society of Photogrammetry and Remote Sensing (ISPRS) launched the first International Contest of ITC Segmentation. The contest aims to reveal the state-of-the-art of the ITC method development using high-resolution images, to clarify the remaining barriers and challenges, and to guide further explorations in the field. This paper overviews the contest and reports the initial finding regarding the impact factors of the method performance.

Improving the Dynamics of an Electrical Drive Using a Modified Controller Structure Accompanied by Delayed Inputs

This paper presents the operation of a modified speed controller with a standard PI/PID structure that includes the preprocessing of the controller’s input signal, focusing on the past behavior of control errors. The modification involves adding a delay line, with the outputs of the individual line segments summed with a weighting method, as detailed in the paper. One of the significant advantages of this method is its use of a standard industrial controller structure, which makes it highly practical and easily implementable in existing systems. By relying on well-established control frameworks, this approach reduces the need for specialized hardware or complex modifications, allowing for smoother integration and lower implementation costs. The delay-based signal shaping shows excellent properties for the electric drive system powered by a hard-switching PWM converter. The set of weighted delays acts as a filter whose parameters are chosen using the quality function to test different configurations for optimal performance. When tested in a speed control system for a Permanent Magnet Synchronous Motor, the modifications improved the control quality index, indicating better performance and efficiency. Importantly, the system allows for reducing or eliminating the gain in the differentiating part of the controller, which decreases motor current chattering and noise. This paper includes an experimental verification of the proposed solution in a laboratory setting under semi-industrial conditions.

Realizing Arbitrary 3D Microarchitectures with Curved and near-Sharp Segments via Toolpath Strategies in Aerosol Jet Printing

Aerosol Jet (AJ) printing is a jetting-based additive manufacturing (AM) technique that uses droplets in an aerosol form to deposit nanoparticles or polymer inks at a length scale of about 10 micrometers. The desired structural geometries via AJ printing can be obtained at a high efficiency and accuracy by engineered toolpaths and fill strategies. Recently, AJ printing is used to create three-dimensional (3D) freestanding microarchitectures such as microlattices, spirals, walls, and micropillars without any auxiliary support during printing. In this work, we demonstrate that for curved segments, the difference in the printed material volume on the convex side vs concave side leads to the accumulation of material while building freestanding 3D microarchitectures. This effect is most severe for sharp corners, which leads to build-defects such as protrusions and voids. We carry out a systematic study of the material accumulation on curved segments of AJ printed 3D microarchitectures. For 3D microwall segments with sharp curved portions, a positive (or negative) material accumulation causing large overgrowths (or voids) as a function of the angle between the tangents on the two sides of the microwalls and the radii of curvature are studied. For sharp bends (<15°) in the microwalls, adding a radius of curvature may not be sufficient to avoid overgrowths and voids, and the lines can be considered as individual segments. For microwall bends with >45°, smaller radii of curvature can be tolerated. We conclude that a fillet radius equal to the line width for 30° angles is necessary to avoid accumulation and therefore the protrusion defects. A simple model based on mass-conservation is also developed which shows the importance of considering ink self-leveling during printing. This information is then used for toolpath strategies to avoid defects on sharp curved microwall segments for complex 3D architectures such as protruding star shape and Scotty Dog, the Carnegie Mellon University mascot. This research will enable the defect-free fabrication of highly complex 3D microarchitectures via jetting-based AM techniques such as AJ printing.

IDA-NET: Individual difference aware medical image segmentation with meta-learning

IDA-NET: Individual difference aware medical image segmentation with meta-learning

Pacing analysis and comparison of TOP-10 and NOT TOP-10 Ultra Trail Cape Town 100-km finishers.

Pacing strategies during endurance efforts vary depending on athlete experience, race length, and terrain. In ultramarathon trail running the specific race route profile can have a significant impact on optimal pacing strategy. The aim of this study was to determine the relationship between uphill, downhill and level running with overall performance and to compare the difference in pacing strategies between top 10 (TOP-10) and non-top 10 (NOT TOP-10) finishers of a 100-km ultra-trail marathon with +5000-m elevation gain. Fifty male finishers (TOP-10: N.=6, NOT TOP-10, N.=44) and forty-six individual segments (1.5±1.2 km) were analyzed based on 2021 100-km UTCT STRAVA data. Relative segment speed and CV% were used to identify pacing strategies. Spearman Rank and Mann Whitney-U tests were used to analyze the data. Significance was set at P<0.05. Uphill running had the strongest relationship with overall performance (r=0.826, P<0.01). Substantially weaker relationships between overall running performance and level (r=0.402, P<0.01) and downhill (r=-0.382, P<0.01) running performance were found. The CV% was significantly lower in TOP-10 (32.10±1.81) than NOT TOP-10 (35.39±4.43) (U=24, r=0.46, P<0.01). Uphill running performance remains a valuable measure of overall trail running performance as it represents a higher percentage of overall running demand. TOP-10 runners have diminished decay in initial running speed than NOT TOP-10 runners, despite a generally high variation in pace and net positive split in ultra-trail running.

Influence of genotype on carcass composition, metric traits of the digestive system and leg bones of laying hens after the egg-production season

Context The genotype of laying hens is one of the most important factors influencing their carcass composition and anatomical features. Aims The aim of the present study was to compare two genotypes of laying hens, Lohmann Brown and Lohmann White, after the laying period. Methods The experimental material consisted of 26 carcasses obtained after slaughtering spent laying hens at the age of 83 weeks. Key results The genotype of the birds had a significant (P &lt; 0.05) effect on carcass weight, and the proportion (%) of the breast muscle, leg muscle and gizzard weight. Significant correlations were also found between bodyweight and the weight of the stomach, proventriculus, liver and spleen. The origin of the laying hens also had a significant impact on the length of the sections on intestinal segments, namely, jejunum, ileum and colon. Significant differences in the correlations between bodyweight and length of duodenum, jejunum and rectum were also found. The compared hybrids of laying hens differed significantly in the dimensions of the femur and tibia. Lohmann Brown (LB) hens were characterized by higher greatest length, medial length, smallest breadth of the corpus, and greatest breadth of the distal end of the femur compared with Lohmann White (LW) hens. Dimensions of the tibia greatest length, axial length, smallest breadth of the corpus, greatest breadth of the distal end and greatest depth of the distal end in LB hens were also significantly (P &lt; 0.05) higher than those in LW hens. Conclusions The results showed that the genotype of laying hens tested after the laying period influenced carcass weight and muscle percentage, as well as differentiated the birds in terms of dimensions of individual segments of the digestive tract, femur, and tibia bones. Implications The results of this research may be useful for consumers of laying hen carcasses and people interested in poultry anatomy.

Mechanical and Geometric Characterization of a Novel 2-Ply Vacuum-Pressed Biological Scaffold Patch Design for Posterior Mitral Valve Reconstruction.

To assess the mechanical properties of small intestinal submucosal extracellular matrix (SIS-ECM) iterations and choose the optimal version for evaluating functional geometrics after posterior mitral valve reconstruction. Four SIS-ECM versions (2- and 4-ply vacuum-pressed and lyophilized) underwent uniaxial tensile testing. A posterior mitral valve reconstruction patch was developed based on MRI scans (n = 5). Posterior mitral valve reconstruction using 2-ply vacuum-pressed SIS-ECM was performed (n = 7), and geometrics were evaluated using a modified left heart simulator. The vacuum-pressed iterations displayed superior maximum stress values compared to lyophilized (2-ply: median [IQR], 15.8 [15.2-19.0] vs 7.9 [7.3-8.3] MPa, p < 0.001; 4-ply: median (IQR), 15.8 -[14.6-22.0] vs 7.9 [7.6-8.4] MPa). All reconstructed valves were competent with preserved total leaflet area, but individual leaflet segment areas were redistributed. Posterior mitral valve reconstruction with our 2-ply vacuum-pressed SIS-ECM patch design was feasible in vitro. Further in vivo evaluation is warranted.

The Role of Gender in Managing Finances among Newly Married Couples in Klang Valley, Malaysia

This study explores the couple's role in managing finances and marital satisfaction from the perspective of newly married couples. By delving into individual demographic and behavioral segments, we reveal how couples manage and plan finances to achieve financial well-being and marital satisfaction, further identifying the role of gender in household financial management. Through a phenomenological method, we conducted semi-structured interviews with seven middle-income newly married couples residing in Klang Valley. Interviews with informants took 6 months between February 2022 and July 2022. The duration of the interview is between sixty and ninety minutes. The interpretation of qualitative data was analyzed thematically using N-Vivo 13 software. The results of the study shed light on the couple's financial management practices to achieve financial well-being and marital satisfaction. The husband is the main pillar in the household's financial affairs. This study offers value in the enrichment of data that reveals couples' understanding of managing finances and plans for their future. By focusing on newly married Malay couples, we gained a deeper insight into simple strategies implemented by household financial managers, division of financial responsibilities, and course of action. Findings that reveal the understanding and behavior of middle-income newlyweds in the Klang Valley towards financial well-being can be used by policymakers, governments, and non-governmental organizations to formulate financial health policies, programs, and campaigns to educate the public about early planning towards financial well-being and enjoying marital satisfaction.

Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score &gt; 80.57%, H Pearson’s r &gt; 0.97, and CD Pearson´s r &gt; 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score &gt; 85% and &gt;76%, and H Pearson´s r &gt; 0.96 and &gt;0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).

Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR.

Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), is of critical importance for immune responses to pathogens and vaccines. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci. Here we present a novel algorithm for extra-sensitive and specific variable (V) and joining (J) gene allele inference, allowing reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput novel allele discovery from a wide variety of existing datasets. The developed algorithm is a part of the MiXCR software. We demonstrate the accuracy of this approach using AIRR-seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of IG heavy chain (IGH) AIRR-seq data from 450 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain (TRA; TRB) AIRR-seq dataset, representing 134 individuals. This allowed us to assess the genetic diversity within the IGH, TRA and TRB loci in different populations and to establish a database of alleles of V and J genes inferred from AIRR-seq data and their population frequencies with free public access through an online database.

The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models

Machine Learning algorithms are widely used by lenders in risk early warning models. With Machine Learning, the risk levels of individual and corporate customers are determined at the account and customer level. Lenders want to manage risk by evaluating the payment performance of customer or account with the help of Machine Learning algorithms. Banks, which have an important place among lenders, develop risk early warning models with the help of learning algorithms using customer information. In the development process of risk early warning models, while banks generally use customer information and credit bureau information for the individual segment, they use financial, non-financial and behaviour-based information for the corporate segment. In this study, it is planned to develop a risk early model for customers in corporate service segment. For the customers of corporate service segment, Balance Sheet and Income Statement items were used and the financial ratios were calculated for risk early warning models. In the development of risk early warning models, Mutual Information method was used as a novel feature selection approach and Support Vector Machine method (linear function, radial basis function and sigmoid function) was used as a supervised learning approach. By changing the neighbourhood metric (k), important patterns were discovered with the Mutual Information method in feature selection process. The optimal C and gamma parameters for Support Vector Machine models have been tried to be determined with the Genetic Algorithm, which is among the Meta-Heuristic algorithms. In order to find the optimal metrics in this study, the metric values for all parameters of the SVM model (function specific) have been kept quite wide. In this dataset of corporate service customers, the small neighbourhood metric has been found to have a significant impact on model learning and performance.

First happiness, then meaning. A cross-cultural investigation into well-being dilemmas

ABSTRACT Dilemmas in navigating between various aspects of well-being are common, but remain unexplored in psychological research. In this paper, we introduce a novel method of assessing well-being dilemmas and their dynamics. In five studies run across the United States, Poland and Japan, we studied the dilemma between the pursuit of happiness and the pursuit of meaning. Our findings revealed common trends across three countries and two research designs: while the majority prioritize happiness, a substantial segment of individuals place a higher value on meaning. Furthermore, these preferences are linked to one’s current state of well-being. As individuals attain a baseline level of both happiness and meaning, the emphasis on happiness diminishes. This suggests a hierarchical framework wherein individuals initially seek to fulfil their happiness before turning their attention to meaningful endeavors. We discuss our findings in the context of previous research on subjective well-being in the cultural context.

Qualitative Analysis of Micro-System-Level Factors Determining Sport Persistence.

Background/Objectives: Sport persistence is the embodiment of sports performance and mental toughness. It refers to our attempts concerning the performance plateau, failures, injuries, or even the resolution and processing of stressful situations associated with success and positive events. In our research, we used qualitative methods based on Bronfenbrenner's socio-ecological model to investigate the factors influencing sport persistence among high school and university athletes. Methods: The research was based on semi-structured interviews with 133 athletes. ATLAS.ti software and the grounded theory methodology were applied for data analysis. Our analysis grouped the responses according to Bronfenbrenner's categorisation system, highlighting motivational factors at the microsystem level. Our research question was as follows: What kind of factors dominate the development of sport persistence among adolescent (high school) and young adult (university) athletes along Bronfenbrenner's dimension of the microsystem? Results: Regarding the microsystem, family, peers, and coaches were mentioned as influential factors. Concerning the family, general, person-specific, family value-related, future-oriented, introjected, and disadvantage-compensating motivational components were identified. General, individual, community and relational factors were identified concerning peers. Concerning the coach, general, individual, community, and coach personality-driven motivational segments were detected. Conclusions: By recognising the complex interplay of systemic factors, we can design interventions targeting these factors at various socio-ecological levels, promoting youth sports and increasing physical activity among young people. These findings instil hope and motivation for the future of sports and physical activity.

Mapping percent canopy cover using individual tree- and area-based procedures that are based on airborne LiDAR data: Case study from an oak-hickory-pine forest in the USA

Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species.

Comprehensive technical analysis of a kickboxing fight in K1 format based on observation

Introduction: Observation and specialized analysis of confrontations in combat sports are fundamental for introducing corrections in training programs and for modifying the individual technical-tactical profiles of athletes in these types of activities. These actions comprehensively assess the progress of sports activities, ultimately inspiring and guiding the direction of training in sports clubs. The aim of this study was to analyze and assess the level of the offensive structure of Kickboxing sport fights in the K1 format, in terms of global simulated sparring, in selected thematic sets. Materials and Methods: The research material consisted of a multimedia recording of 10 simulated K1 sparring sessions, in which 20 professional athletes of this discipline participated (age: 24.5 ± 4.6 years; body height: 179.1 ± 4.6 cm; body weight: 81.7 ± 9.9 kg; BMI: 25.5 ± 3.7; training experience: 6.9 ± 1.3 years). To assess the offensive structure of the fight, a retrospective analysis of the recorded empirical material was conducted in terms of the quantity of attacks made, and then specialized technical-tactical preparation (PTT) indicators were calculated, in the global context of sparring, for thematic sets (total; punches vs kicks; right vs left limb attacks; type of techniques; direction of attack). Results: The analysis revealed a significantly higher technical-tactical efficiency regarding hand strikes, left hand, and direction of strikes to the opponent's head in terms of activity (p &lt; 0.001), effectiveness (p &lt; 0.001), and efficiency (p = 0.008–0.408) of attack. In isolated analysis of kicking techniques, a significant advantage in efficiency was registered for selected attacks directed at the lower parts of the opponent's body, i.e., torso, legs (p =&lt; 0.001–0.043). The most effective and exploited techniques were: left straight (Aa x̃=36.8; Sa x̃=23.9), and for kicks, right low kick (Aa x̃=14.9; Sa x̃=5.6). The highest attack efficiency was noted for the right middle kick (Ea x̃=54.18). Several selected comparative sets (inter-limb symmetry, type of attack, direction of attack) for technical-tactical efficiency, were characterized by significant statistical differentiation (p=&lt;0.001–0.048). Conclusions: Kickboxing is an asymmetrical combat sport, which necessitates the application of targeted training on individual body segments of the athlete, and compensatory actions in the prevention of injuries. The study results allow for detailed diagnosis and interpretation of the technical-tactical profile along with the key manifestation of offensive competencies in Kickboxing profession in the K1 format, favoring the optimization of the quality of coaching control.

Optical Surface Roughness Estimation Using Artificial Intelligence

ABSTRACTA system for analysing steel plates is presented for an application in the wind power industry. The objective of the system is to assess the surface roughness as a preliminary step for the application of an anti‐corrosion coating. A number of noncontact sensor systems were considered, with the laser triangulation scanner proving to be the optimal solution. The samples are analysed in a test stand and the roughness is measured with this optical system. At the same time, the surface is recorded with a CMOS camera. An AI model is created from the sensor fusion of both systems, which can classify individual segments of the surface as well or poorly blasted. In the following step, the surface roughness is estimated as the Rz parameter by another AI model. An error of less than 6 μm is achieved.

YOLO-SegNet: A Method for Individual Street Tree Segmentation Based on the Improved YOLOv8 and the SegFormer Network

In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this paper, based on a large, publicly available urban street tree dataset, proposes YOLO-SegNet for individual street tree segmentation. In the first stage of the street tree object detection task, the BiFormer attention mechanism was introduced into the YOLOv8 network to increase the contextual information extraction and improve the ability of the network to detect multiscale and multishaped targets. In the second-stage street tree segmentation task, the SegFormer network was proposed to obtain street tree edge information more efficiently. The experimental results indicate that our proposed YOLO-SegNet method, which combines YOLOv8+BiFormer and SegFormer, achieved a 92.0% mean intersection over union (mIoU), 95.9% mean pixel accuracy (mPA), and 97.4% accuracy on a large, publicly available urban street tree dataset. Compared with those of the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPNet), UNet, DeepLabv3+, and HRNet, the mIoUs of our YOLO-SegNet increased by 10.5, 9.7, 5.0, 6.8, 4.5, and 2.7 percentage points, respectively. The proposed method can effectively support smart agroforestry development.

Three-Dimensional Reconstruction of Forest Scenes with Tree–Shrub–Grass Structure Using Airborne LiDAR Point Cloud

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground–grass points from an airborne LiDAR point cloud. For ground–grass points, a ground–grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D α-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D α-shape algorithm. Finally, tree, shrub, and ground–grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-Württemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE &lt; 0.15 m, the volume accuracy of shrub and tree models is assessed with R2 &gt; 0.848 and R2 &gt; 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes.

Kinematic and aerodynamic analysis of a Microraptor-inspired foldable wing mechanism

Bio-inspired mechanisms have opened new avenues of research to understand the flying capabilities of birds. While extensive studies have explored the aerodynamics and kinematics of bird flapping motions, limited attention has been given to the folding motion. This paper introduces a foldable wing mechanism inspired by avian anatomy, specifically drawing inspiration from Microraptor aerodynamics. Employing the screw theory approach, we analyze the kinematics of the mechanism and conduct parametric calculations. By segmenting the mechanism and integrating the solutions, we develop an algorithm that demonstrates favorable power transmission, enabling the wing to remain extended with minimal actuation forces. We further employ position analysis to investigate the system’s functionality analytically. To optimize the mechanism, we utilize the gradient descent method. We validate the achieved momentum and velocities through an illustrative example by solving individual four-bar segments and transmitting the outputs to adjacent segments. The study utilizes detailed 3D modeling techniques employing SolidWorks software to accurately represent the wing designs. The results obtained provide valuable insights into the aerodynamic behavior of Microraptor, by comparing the coefficients of lift and drag in graphical form and contribute to the advancement of micro-aerial vehicle design.

Magnetic resonance neurography in the diagnosis of neurological subtypes of thoracic outlet syndrome.

Neurological thoracic outlet syndrome (TOS) can be challenging to diagnose, particularly given its described subtypes of neurogenic TOS (NTOS) and disputed TOS (DTOS) that exhibit variable clinical presentations and etiologies. The diagnostic workup of TOS often includes magnetic resonance neurography (MRN) of the brachial plexus. Specific MRN imaging modifications for TOS evaluation are required to maximize spatial and contrast resolution to increase the conspicuity of nerve segments and their relationships to surrounding osseous structures. Dynamic assessment with arm positioning is used to evaluate outlet narrowing and compression of the plexus. Individual nerve segments are interrogated for their longitudinal and cross-sectional morphologies and signal characteristics. In patients with NTOS, MRN may reveal focal impingement of the C8/T1 nerve roots and/or lower trunk with accompanying abnormal T2-weighted signal hyperintensity. Predisposing anatomical entities include cervical ribs, rib synostoses, hypertrophic callous following clavicular fracture, remnant first thoracic rib from prior incomplete resection, and variable perineural scarring. In comparison, DTOS patients frequently demonstrate signal hyperintensity and enlargement of the mid plexus (trunk and division level), with narrowing of the costoclavicular interval. Following comprehensive diagnostic workup that frequently includes electrodiagnostic testing, patients are directed to different management pathways. Nonsurgical management is considered for all cases of DTOS; all patients with NTOS or DTOS who fail conservative treatment warrant referral for a surgical opinion. If surgery is pursued, MRN can be helpful in preoperative planning.