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Blue-light imaging has an additional value to white-light endoscopy in visualization of early Barrett's neoplasia: an international multicenter cohort study
摘要： Endoscopic features of early neoplasia in Barrett’s esophagus (BE) are subtle. Blue-light imaging (BLI) may improve visualization of neoplastic lesions. The aim of this study was to evaluate BLI in visualization of Barrett’s neoplasia. Methods: Corresponding white-light endoscopy (WLE) and BLI images of 40 BE lesions were obtained prospectively and assessed by 6 international experts in 3 assessments. Each assessment consisted of overview and magnification images. Assessments were as follows: assessment 1, WLE only; assessment 2, BLI only; and assessment 3, corresponding WLE and BLI images. Outcome parameters were as follows: (1) appreciation of macroscopic appearance and surface relief (visual analog scale scores); (2) ability to delineate lesions (visual analog scale scores); (3) preferred technique for delineation (ordinal scores); and (4) quantitative agreement on delineations (AND/OR scores). Results: Experts appreciated BLI significantly better than WLE for visualization of macroscopic appearance (median 8.0 vs 7.0, P < .001) and surface relief (8.0 vs 6.0, P < .001). For both overview and magnification images, experts appreciated BLI significantly better than WLE for ability to delineate lesions (8.0 vs 6.0, P < .001 and 8.0 vs 5.0, P < .001). There was no overall significant difference in AND/OR scores of WLE + BLI when compared with WLE, yet agreement increased significantly with WLE + BLI for cases with a low baseline AND/OR score on WLE, both in overview (mean difference, 0.15; P = .015) and magnification (mean difference, 0.10; P = .01). Conclusions: BLI has additional value for visualization of BE neoplasia. Experts appreciated BLI better than WLE for visualization and delineation of BE neoplasia. Quantitative agreement increased significantly when BLI was offered next to WLE for lesions that were hard to delineate with WLE alone.
关键词： neoplasia,white-light endoscopy,visualization,Barrett's esophagus,delineation,blue-light imaging
更新于2025-09-23 15:23:52
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A Local Projection-Based Approach to Individual Tree Detection and 3-D Crown Delineation in Multistoried Coniferous Forests Using High-Density Airborne LiDAR Data
摘要： Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method.
关键词： forest,3-D tree crown delineation,tree top detection,airborne laser scanner,Light Detection and Ranging (LiDAR)
更新于2025-09-23 15:21:01
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[IEEE ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, AB (2018.4.15-2018.4.20)] 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Robust Beat-To-Beat Detection Algorithm for Pulse Rate Variability Analysis from Wrist Photoplethysmography Signals
摘要： Heart rate variability (HRV) from electrocardiograms (ECG) is a well-known diagnostic method for the assessment of autonomic nervous function of the heart. A more convenient approach to assess cardiac function is by using Photoplethysmography (PPG) waveforms where pulse rate variability (PRV) replaces HRV. However, the unavailability of robust detection algorithms for PPG signals has prevented the medical market from providing clinical diagnosis using PRV and from measuring biological information for wellness purposes, such as sleep stage, stress state, and fatigue. This paper provides a robust peak and onset detection algorithm for beat-to-beat (B2B) pulse interval analysis using PPG signals. We demonstrate our method through large data collection with the Analog Devices (ADI) multi-sensory watch platform with high coverage, sensitivity, and low Root Mean Square of Successive Difference (RMSSD) as compared to the B2B results from ECG signals.
关键词： Delineation,Pulse Rate Variability (PRV),Heart Rate Variability,Beat-to-Beat,Photoplethysmography (PPG)
更新于2025-09-10 09:29:36
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Repeatability of <sup>18</sup> F-FDG PET Radiomic Features: a Phantom Study to Explore Sensitivity to Image Reconstruction Settings, Noise, and Delineation Method
摘要： Background: 18F-fluoro-2-deoxy-D-Glucose positron emission tomography (18F-FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18F-FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g. object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method.
关键词： 18F-FDG PET/CT radiomic features,delineation,image reconstruction settings
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Assessment of Tree Attributes Extraction Algorithms
摘要： Canopy Height Model (CHM)-based and point-based tree extraction algorithms are two common techniques to extract individual trees from airborne lidar data. In general, point-based algorithms process lidar points directly but suffer from intensive computation while CHM-based algorithms are efficient but fail to extract sub-dominant trees in dense canopies and their performances rely on the resolution of the CHM. To test which existing algorithm yields most accurate results, this paper compares Fixed-window local maxima algorithm, Popescu and Wynne’s local maxima algorithm, variable area local maxima algorithm, individual-tree-crown delineation algorithm (ITC) and Li’s point-based segmentation algorithm (LPS). The comparisons of the results to the reference data indicate although LPS extracts the largest number of individual trees, the extracted tree heights and crown widths are less accurate since LPS suffers from oversegmentation. In contrast, ITC outperforms other algorithms when measured by extracted tree heights and crown widths due to the adaptive window size. To testify if CHMs with better resolution promote the accuracy of extracted tree heights and crown widths since they provide more details, CHM-based algorithms are also applied to CHMs with different resolutions. The results indicate that although CHMs with better resolution help to yield more accurate tree heights, they do not necessarily result in more accurate crown widths.
关键词： lidar,tree extraction,crown delineation,forest
更新于2025-09-09 09:28:46
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Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students
摘要： Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this work investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. 76 medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: (1) Labeling of images showing tissue regions, and (2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants’ performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology.
关键词： annotation,image classification,confidence score,image delineation,human decision making,Crowdsourcing,digital pathology,majority vote
更新于2025-09-09 09:28:46