Method For Delineation Research Articles

Instance Segmentation XXL-CT Challenge of a Historic Airplane

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the ‘Instance Segmentation XXL-CT Challenge of a Historic Airplane’ was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.

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.

Individual Tree Crown Delineation Using Airborne LiDAR Data and Aerial Imagery in the Taiga–Tundra Ecotone

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate (Ar) of 96.60%. Compared to the two conventional ITC delineation methods, the Ar of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time.

Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis

Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly.

Research on the Delineation Method of Urban Community Living Circle Based on Transportation Network Analysis in Yuanping, China

In the context of accelerating global urbanization and the demands for high-quality and sustainable development, the exploration of an urban community living area delineation method based on traffic network analysis offers a fresh perspective for defining urban living areas. This paper employs a traffic network analysis method rooted in objective analysis. It leverages open-source big data on Points of Interest (POI) from public facilities and road network data obtained from relevant urban planning to construct a comprehensive road network dataset for the central urban area. Following the construction of the road network dataset, the paper conducts a service area and accessibility analysis of public facilities, which serves as an initial step in outlining the boundaries of the urban community living area. Subsequently, the delineation is further refined by considering factors such as development boundaries, as well as natural and man-made constraints such as rivers, railways, and major roads. The boundaries of the urban community living area undergo further adjustments, taking into account the scope of the development boundary and various constraining elements such as rivers, railways, and main roads. A final revision is made by considering subjective factors, where the boundaries of the ten-minute urban community living area are ultimately determined through extensive communication and coordination with various stakeholders. This approach is aimed at meeting the current societal development needs, which include breaking down segregation, fostering social interactions, strengthening neighbourly relationships, enhancing overall quality of life, promoting sustainable development, and creating a more humane and sustainable city. Its ultimate goal is to enable residents to feel a sense of belonging and happiness within the bustling urban environment.

Identification and Biological Characterization of Green Alga on Oil-Tea Camellia Leaves

Between 2023 and 2024, a type of green alga was observed for the first time settling on Oil-tea Camellia leaves and branches in the eastern Oil-tea Camellia planting area of Hainan Island, forming a layer of gray-green moss with a rough surface that seriously interfered with the leaves’ normal photosynthesis. To further research the issue, this study used the plant photosynthesis measurement system and the paraffin sectioning technique to compare and analyze the changes in photosynthetic characteristics and anatomical structure of healthy and green algal-covered Oil-tea Camellia leaves. At the same time, the algal strain was effectively separated and purified using the plate delineation method, and its species classification was determined by combining morphological observation and molecular identification based on SSU-ITS sequences. The results of the study demonstrated that the coating of green alga facilitated the lignification of the leaf’s epidermal cell walls. After being covered by the green alga, the intercellular CO2 concentration (Ci) increased significantly by 21.5%, while the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs) all significantly decreased by 72.8%, 30.4%, and 24.9%, respectively. More specifically, the green alga that covers the leaves of Oil-tea Camellia was identified as Desmodesmus armatus of Chlorophyta. Notably, the green alga had a long growth cycle, did not undergo a decline phase within one month, had an optimal growth pH of 11.0, and could flourish in excessively alkaline conditions. In conclusion, this study not only reported for the first time the phenomena of D. armatus infesting Oil-tea Camellia leave, but also showed its unique physiological and ecological properties, providing a foundation for future research on relevant prevention and control approaches.

Exploration of the Urbanization Process and Its Impact on Vegetation in 125 Resource-Based Cities in China and Comparison with Other Cities

Resource-based cities (RBCs) in China are at a historic juncture in their transformative development. Observing and assessing the role of the resource curse in urban expansion and greening is crucial for the sustainable development of these cities. This study proposes a new framework to extract urban boundary data from 2000 to 2020 in China. Utilizing these data, we analyzed differences in urban expansion intensity and urban vegetation cover between 125 RBCs and 223 non-RBCs. We found the following: (1) While urban areas in China experienced steady growth from 2000 to 2020, the urban area expansion rates of RBCs lagged behind those non-RBCs located in the same geographical areas except in South China, with the lowest annual expansion rate of 1.18% occurring in the Northeast. (2) Although the existing urban areas in some cities show a greening trend, both existing and new urban areas in China are predominantly characterized overall by browning. Surprisingly, RBCs exhibit a stronger greening trend than non-RBCs, particularly in Northwestern China. (3) There is a nuanced interplay and coexistence between resource dependency and urban expansion, with a specific negative correlation when resource dependency is very high or very low. This study provides a novel method and approach for urban boundary delineation. It offers new insights into the developmental characteristics of RBCs, enriching the theoretical framework of resource curse research and supporting the green development of RBCs with robust data.

Cadastral-to-Agricultural: A Study on the Feasibility of Using Cadastral Parcels for Agricultural Land Parcel Delineation

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F1-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land.

The geography of healthcare: Mapping patient flow and medical resource allocation in China

The misallocation of medical resources leads to interregional patient flow in search of better healthcare. Using out-of-pocket medical expenditure data and a delineating method, this paper identifies spatial clusters of medical services in China based on patient flow across cities. Our findings indicate that healthcare resources are more concentrated in northern China, while southern China is divided into several large healthcare clusters at the same threshold. The provincial capital and economically significant cities are more likely to serve as medical cluster centers. We further apply the gravity model to examine the effects of healthcare disparity on cross-city medical expenditure. The results reveal that geographic disparities in high-quality medical resources encourage remote healthcare-seeking behavior, and the shorter the distance between locations, the higher the level of medical consumption. Patients are inclined to seek medical services within their own province and within specific medical clusters identified through delineation methods. This effect is more pronounced among patients from non-central cities. This study highlights healthcare inequality by examining cross-regional medical expenditure, providing valuable insights for future healthcare policy.

Evolution Process and Land Use/Land Cover Response of Urban–Rural Space in Wuhan under Polycentric Structure

Polycentric development facilitates urban–rural spatial reshaping and land use/land cover (LULC) protection. Previous studies have predominantly focused on urban areas, with spatial delineation methods biased towards the macro-level, lacking a holistic perspective that situates them within the urban–rural spatial framework. This study proposes a spatial delineation framework that is applicable to the polycentric structure, taking into account the social, economic, and natural characteristics of urbanization. It employs semivariance analysis and spatial continuous wavelet transform (SCWT) to analyze the effects of polycentric development on the urban–rural space of Wuhan from 2012 to 2021 and applies a land use transition matrix, landscape indices, and bivariate spatial autocorrelation to quantify the responses and differences of LULC within urban–rural space. The results indicate that 600m×600m is the best scale for exhibiting the multidimensional characterization of urbanization. The polycentric structure alleviates the compact development of the central city, and it drives rapid expansion at the urban–rural fringe, exacerbating the spatial heterogeneity in LULC change pattern, spatial configuration, and urbanization response within urban–rural spaces. The overall effects of urbanization on LULC are relatively weak along the urban–rural gradient, experiencing a transition from positive to negative and back to positive. This study employs a novel spatial delineation framework to depict the polycentric transformation of metropolitan areas and provides valuable insights for regional planning and ecological conservation in the urban–rural fringe.

Morphological and molecular characterization of the green algae Ulva in the Mediterranean Coast of Egypt

The green algal genus Ulva Linnaeus is a model organism for studying morphogenesis, development, and life cycles. Ulva is referred to as cryptic species; with limited external diagnostic features, yet genetically different. There have been limited molecular data available for seaweeds in the Mediterranean Coast of Egypt. This work aimed to delineate Ulva spp. by combining morphological examination and molecular-species-delimitation approaches including Assemble Species by Automatic Partitioning (ASAP, based on distance) and generalized mixed Yule-coalescent (GMYC, based on coalescence) using three genes; ITS, tufA, and rbcL. Species delimitation approaches have identified different partitions based on each gene, either by assigning similar species into a single partition using ASAP or by over splitting/lumping species using GMYC. Sequence-based identity comparisons of the investigated specimens with Ulva sequences available at NCBI revealed similarities with six Ulva species based on ITS, four species using tufA, and five species using rbcL. Additionally, five samples remained unidentified at the species level, suggesting a potential new species, which requires further comprehensive analysis. Based on phylogenetic analysis, the investigated species were placed at varying distances from different Ulva type specimens, as well as similar and dissimilar reference sequences. This variance can be attributed primarily to the absence of adequate type specimen sequences and/or the presence of misidentified entries within the NCBI database. Nucleotide diversity parameters revealed the highest sequence diversity of tufA, compared to ITS and rbcL. Combining morphological-and genetic analyses in Ulva spp. delimitation revealed the presence of U. fasciata, U. flexuosa,U. intestinalis, U. lactuca, U. linza, U. rigida, and Ulva sp. The current study enhances our comprehension of Ulva species diversity and underscores the significance of integrating molecular and morphological approaches for species delineation. This integration plays a pivotal role in addressing species complexes and precisely defining species boundaries.

2766: Automatic segmentation method for OARs and GTVs delineation for nasopharyngeal carcinoma treatments

2766: Automatic segmentation method for OARs and GTVs delineation for nasopharyngeal carcinoma treatments

2457: Comparison of PET based semi-automatic delineation methods with histopathology as reference

2457: Comparison of PET based semi-automatic delineation methods with histopathology as reference

2945: Simultaneous integrated boost in breast radiotherapy: Comparison of delineation methods

2945: Simultaneous integrated boost in breast radiotherapy: Comparison of delineation methods

Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors.

Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information for tumor identification and was investigated in comparison with RS for delineating brain tumors in situ. Raman spectra were acquired together with AF in situ within the solid tumor and at the tumor border during routine brain tumor surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, n = 10; metastases, n = 10; meningioma, n = 3). Tissue classification for tumor identification in situ was trained on ex vivo data (375 spectra; glioma/GBM patients, n = 20; metastases, n = 11; meningioma, n = 13; and epileptic hippocampi, n = 4). Both in situ and ex vivo data showed that AF intensity in brain tumors was lower than that in border regions and normal brain tissue. Moreover, a positive correlation was observed between the AF intensity and the intensity of the Raman band corresponding to lipids at 1437cm- 1, while a negative correlation was found with the intensity of the protein band at 1260cm- 1. The classification of in situ AF and RS datasets matched the surgeon's evaluation of tissue type, with correct rates of 0.83 and 0.84, respectively. Similar correct rates were achieved in comparison to histopathology of tissue biopsies resected in selected measurement positions (AF: 0.80, RS: 0.83). Spectroscopy was successfully integrated into existing neurosurgical workflows, and in situ spectroscopic data could be classified based on ex vivo data. RS confirmed its ability to detect brain tumors, while AF emerged as a competitive method for intraoperative tumor delineation.

Sky-view factor enhanced doline delineation: A comparative methodological review based on case studies in Slovenia

This study presents a novel method for the accurate delineation of dolines in karst areas. The method is based on the use of hydrological tools and sky-view Factor in ArcGIS Pro and was implemented using a digital elevation model with a resolution of 1 m in four study areas in Slovenia. We manually delineated dolines at four test areas and compared them with the results of the new method, as well as the results of the most commonly used method of hydrological filling and the results of U-Net segmentation. We calculated the average deviation of the perimeter and the differences in the basic morphometric properties. The hydrological filling method cannot be accepted as a suitable method for doline delineation and should only be used for doline identification. The method based on the U-Net segmentation performed better, but the results contain landforms that are not enclosed depressions and therefore cannot be considered as dolines. The new method utilizes the advantages of hydrological filling and at the same time improves the doline delineation. We conclude that the presented method is the most suitable for automatic doline identification and delineation among the automatic methods discussed in this paper. This study further quantifies the enhancements achieved with the new method, highlighting the specific improvements in perimeter accuracy and the reliability of morphometric measurements.

Towards more consistent volcano morphometry datasets: Assessing boundary delineation and DEM impact on geometric and drainage parameters

Composite volcanoes are dynamic landforms that require comprehensive morphological analysis to understand their formation, degradation and associated controlling processes. Establishing Digital Elevation Model (DEM) source, spatial resolution and edifice delineation method are the first essential steps to quantify volcano morphometry. The delineation and quantification of the morphology is a complex endeavor as a volcano’s edifice is the result of overlapping eruptive products, intrusions, and degradation by a range of erosional processes and thus needs to be assessed in different volcanic environments.In this study, sixteen volcanoes from four volcanic arcs are used to quantify and compare twelve different geometric and drainage parameters. We first perform an edifice delineation analysis where we compare the similarity of volcano boundaries drawn by seven volcano geomorphology experts. Afterwards, a second set of boundaries is drawn based on a slope threshold to guide the delineation between boundaries, which proves beneficial in enhancing consistency between expert-driven manual delineation. For the same volcanoes, we also extract automatic delineation algorithm-derived NETVOLC boundaries to complement the comparison. Afterwards, a comparative analysis of morphologic parameters is conducted for four free and globally available 30-m-resolution DEMs: ALOS (AW3D30), SRTM (SRTMGL1), ASTER (GDEM 003) and TanDEM-X. The impact of resolution is also assessed using the 12 m and 30 m grid TanDEM-X DEMs on the same parameters.Our results show that precise and consistent delineation of the volcanic edifice boundaries, and to a lesser degree the resolution of the DEM, holds greater significance than the specific DEM type used to extract morphometric parameters. The slope cost function of NETVOLC shows the lowest deviation from the expert-defined boundaries. The metrics most sensitive to the defined boundary are volumes and basin width, and the parameters that significantly differ between 12 m and 30 m TanDEM-X are irregularity index, eroded volume, slope and drainage density. Our analysis thus emphasizes the necessity of meticulous consideration when selecting the DEM, and more importantly, adopting a consistent approach to delineating edifice boundaries in comparative morphometric analyses of volcanoes.

Chemotaxonomy compared to morphological and anatomical taxonomy of five Hibiscus species

Hibiscus is a charismatic genus of the Malvaceae family that is noted for its diversity, lacking identifiable characteristics for distinguishing its various species. Therefore, there is an urgent need to develop authentication methods for genus delimitation and species delineation. The present study aims to discern the taxonomic relationships between the well-known, globally familiar, and economically important five Hibiscus species, namely: H. × rosa-sinensis, H. sabdariffa, H. schizopetalus, H. syriacus and H. tiliaceus based on traditional morphological and anatomical characteristics compared to the contemporary chemotaxonomy. In this context, the leaf-based methanolic extracts of the studied species were analyzed by Gas Chromatography-Mass Spectrometer (GC–MS) to estimate their secondary metabolites similarity. In addition, selected qualitative morphological and anatomical traits including leaf venation patterns, epidermal micromorphology, stomata types and trichomes diversity, petiole serial sectioning (outline, adaxial groove features, vasculature traces arrangement), and midrib characteristics of the studied species were investigated. The results of both chemotaxonomy and traditional taxonomy exhibited a remarkable agreement in the delineation of the five studied species. Specifically, the chemotaxonomy-based dendrogram separates the studied species into two main clusters with the H. sabdariffa as an outlier species in a single cluster and the remaining four species as another cluster with variant distances in its similarity indices. Similarly, the traditional morphological and anatomical characteristics revealed distinct traits for H. sabdariffa compared to the remaining four species. The findings of this study highlight the significance of integrating the structural features with phytochemicals profiling as a potential approach that could be harnessed for the delineation of the taxonomically challenging Hibiscus genus.

International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation.

Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark datasetallowing TMTV to be tested and applied as a reproducible biomarker. Methods: Sixty baseline 18F-FDG PET/CT scans were identified with a range of disease distributions (20 follicular, 20 Hodgkin, and 20 diffuse large B-cell lymphoma). TMTV was measured by 12 nuclear medicine experts, each analyzing 20 cases split across subtypes, with each case processed by 3-4 readers. LIFEx or ACCURATE software was chosen according to reader preference. Analysis was performed stepwise: TMTV1 with automated preselection of lesions using an SUV of at least 4 and a volume of at least 3 cm3 with single-click removal of physiologic uptake; TMTV2 with additional removal of reactive bone marrow and spleen with single clicks; TMTV3 with manual editing to remove other physiologic uptake, if required; and TMTV4 with optional addition of lesions using mouse clicks with an SUV of at least 4 (no volume threshold). Results: The final TMTV (TMTV4) ranged from 8 to 2,288 cm3, showing excellent agreement among all readers in 87% of cases (52/60) with a difference of less than 10% or less than 10 cm3 In 70% of the cases, TMTV4 equaled TMTV1, requiring no additional reader interaction. Differences in the TMTV4 were exclusively related to reader interpretation of lesion inclusion or physiologic high-uptake region removal, not to the choice of software. For 5 cases, large TMTV differences (>25%) were due to disagreement about inclusion of diffuse splenic uptake. Conclusion: The proposed segmentation method enabled highly reproducible TMTV measurements, with minimal reader interaction in 70% of the patients. The inclusion or exclusion of diffuse splenic uptake requires definition of specific criteria according to lymphoma subtype. The publicly available proposed benchmark allows comparison of study results and could serve as a reference to test improvements using other segmentation approaches.

174 Development of deep learning based delineation method of clinical target volume for lung tumor

174 Development of deep learning based delineation method of clinical target volume for lung tumor