التفاصيل البيبلوغرافية
| العنوان: |
Evaluating SSAM error in lung space volume compared to ground truth lungs from the LUNA16 dataset. |
| المؤلفون: |
Josh Williams, Haavard Ahlqvist, Alexander Cunningham, Andrew Kirby, Ira Katz, John Fleming, Joy Conway, Steve Cunningham, Ali Ozel, Uwe Wolfram |
| سنة النشر: |
2024 |
| المجموعة: |
Smithsonian Institution: Figshare |
| مصطلحات موضوعية: |
Medicine, Cell Biology, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, one billion sufferers, median error compared, inhalers crucially influences, image processing approach, ground truth segmentations, assessed deposition compared, 3d medical images, 3d ct images, image processing framework, statistical shape models, lung outline noise, automated computational framework, specific deposition modelling, div >< p, 2d chest x, lung morphology produced, specific deposition measurements, airway morphology ), specific modelling, computational models, specific features, lung pathology, proposed framework, vivo <, varying treatments |
| الوصف: |
(a) Relative error from SSAM and U-Net reconstructions of patients from LUNA16 dataset. Inset shows U-Net lung space volume error, where the U-Net results were produced using a pretrained U-Net [ 12 ]. (b) Identity plot comparing absolute lung space volumes from SSAM and ground truth. CCC is the concordance correlation coefficient. |
| نوع الوثيقة: |
still image |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/figure/Evaluating_SSAM_error_in_lung_space_volume_compared_to_ground_truth_lungs_from_the_LUNA16_dataset_/25076581 |
| DOI: |
10.1371/journal.pone.0297437.g004 |
| الاتاحة: |
https://doi.org/10.1371/journal.pone.0297437.g004 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.741345C6 |
| قاعدة البيانات: |
BASE |