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  1. 5461
    “…Using the publically available LA benchmark database, we compared the proposed joint‐atlas‐optimization approach to the conventional pairwise atlas approach and evaluated the segmentation performance in terms of Dice index and surface‐to‐surface (S2S) distance to the manual ground truth. RESULTS: The proposed joint‐atlas‐optimization method showed systemically improved accuracy and robustness over the pairwise atlas approach. …”
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    Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint‐atlas‐optimization
  2. 5462
    “…By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. …”
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    Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation
  3. 5463
    “…We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. …”
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    Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images
  4. 5464
    “…RESULTS: We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. …”
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    Online Artículo Texto
    Automated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data
  5. 5465
    “…However, DTI tractography is challenged by inherent presumptions during particular tracking steps [e.g., deterministic vs. probabilistic DTI, fractional anisotropy (FA) and fiber length (FL) thresholding] and the missing “ground truth” information. In the present study, we intended to establish an objective, neurophysiology-driven approach for parameter selection during DTI-FT of the corticospinal tract integrating both imaging and neurophysiological information. …”
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    Neurophysiology-Driven Parameter Selection in nTMS-Based DTI Tractography: A Multidimensional Mathematical Model
  6. 5466
    “…Interobserver agreement using kappa value was calculated for both the sets and two out of three agreements for DR grading was considered as ground truth to compare with AI results. RESULTS: In the internal validation set, the overall sensitivity and specificity was 99.7% and 98.5% for Any DR detection and 98.9% and 94.84%for Prompt referral respectively. …”
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    Online Artículo Texto
    Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening
  7. 5467
    “…We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. …”
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    Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm
  8. 5468
    “…Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. …”
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    Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective
  9. 5469
    “…Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max‐pooling layers and ended with three dense layers. …”
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    Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method
  10. 5470
    “…The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. …”
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    ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods
  11. 5471
    “…All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). …”
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    Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
  12. 5472
    “…However, because we do not know the truth, we cannot conclude whether to normalize and recommend incorporating both.…”
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    Surface Electromyography Normalization Affects the Interpretation of Muscle Activity and Coactivation in Children With Cerebral Palsy During Walking
  13. 5473
    “…The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. …”
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    Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
  14. 5474
    “…We used mutation data to establish molecular ground truth and subdivide lesions into LI/LD tumour subsets. …”
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    Exploiting differential Wnt target gene expression to generate a molecular biomarker for colorectal cancer stratification
  15. 5475
    “…METHODS: A dataset of incidentally detected pulmonary nodules measuring 5–15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. …”
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    External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules
  16. 5476
    “…In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season. …”
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    Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean
  17. 5477
    “…Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964–0.968 and mean errors of 2.2–4.0 mg/cm(3). …”
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    Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening
  18. 5478
    “…Considering CT as the ground truth, the detection rates of subependymal nodules using conventional MRI and QSM were compared by the paired Chi-square test, and the sensitivity and specificity were computed. …”
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    Investigation of quantitative susceptibility mapping in diagnosis of tuberous sclerosis complex and assessment of associated brain injuries at 1.5 Tesla
  19. 5479
    “…METHOD: We searched the follow databases, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), PsycINFO, WEB OF SCIENCE, Embase, CBM (Chinese Biomedical Literature database), WANFANG database (Chinese Medicine Premier), and CNKI (China National Knowledge Infrastructure), using the following terms: neoplasm, cancer, tumor, tumor, carcinoma, disclosure, truth telling, breaking bad news, knowledge, knowing, awareness, quality of life, QOL. …”
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    The impact on quality of life from informing diagnosis in patients with cancer: a systematic review and meta-analysis
  20. 5480
    “…Our experimental results including a direct comparison with manually segmented ground truth data show that images of different plant types acquired at different developmental stages from different camera views can be automatically segmented with the average accuracy of [Formula: see text] ([Formula: see text] ) using our two-step registration-classification approach. …”
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    A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping
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