Samuel Armato to Lung
This is a "connection" page, showing publications Samuel Armato has written about Lung.
Connection Strength
2.400
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Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans. J Digit Imaging. 2015 Dec; 28(6):755-60.
Score: 0.292
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Lung texture in serial thoracic CT scans: assessment of change introduced by image registration. Med Phys. 2012 Aug; 39(8):4679-90.
Score: 0.232
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The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011 Feb; 38(2):915-31.
Score: 0.209
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A modified gradient correlation filter for image segmentation: application to airway and bowel. Med Phys. 2009 Feb; 36(2):480-5.
Score: 0.182
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Two-dimensional extrapolation methods for texture analysis on CT scans. Med Phys. 2007 Sep; 34(9):3465-72.
Score: 0.165
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Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. J Digit Imaging. 2021 08; 34(4):922-931.
Score: 0.108
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Deep Learning Demonstrates Potential for Lung Cancer Detection in Chest Radiography. Radiology. 2020 12; 297(3):697-698.
Score: 0.102
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Automated registration of frontal and lateral radionuclide lung scans with digital chest radiographs. Acad Radiol. 2000 Jul; 7(7):530-9.
Score: 0.100
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Computerized detection of pulmonary nodules on CT scans. Radiographics. 1999 Sep-Oct; 19(5):1303-11.
Score: 0.095
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Automated lung segmentation in digital lateral chest radiographs. Med Phys. 1998 Aug; 25(8):1507-20.
Score: 0.088
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Computerized delineation and analysis of costophrenic angles in digital chest radiographs. Acad Radiol. 1998 May; 5(5):329-35.
Score: 0.086
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Automated lung segmentation in digitized posteroanterior chest radiographs. Acad Radiol. 1998 Apr; 5(4):245-55.
Score: 0.086
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Automated registration of ventilation-perfusion images with digital chest radiographs. Acad Radiol. 1997 Mar; 4(3):183-92.
Score: 0.080
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Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis. J Digit Imaging. 2015 Dec; 28(6):704-17.
Score: 0.073
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Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys. 2015 Apr 01; 91(5):1048-56.
Score: 0.069
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Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy. Phys Med Biol. 2014 Sep 21; 59(18):5387-98.
Score: 0.067
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Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys. 2013 Jun; 40(6):061906.
Score: 0.061
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The influence of initial outlines on manual segmentation. Med Phys. 2010 May; 37(5):2153-8.
Score: 0.050
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Temporal subtraction in chest radiography: mutual information as a measure of image quality. Med Phys. 2009 Dec; 36(12):5675-82.
Score: 0.048
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Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections. Med Phys. 2006 Sep; 33(9):3085-93.
Score: 0.039
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Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans Med Imaging. 2005 Apr; 24(4):486-99.
Score: 0.035
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Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology. 2002 Dec; 225(3):673-83.
Score: 0.030
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Automated detection of lung nodules in CT scans: preliminary results. Med Phys. 2001 Aug; 28(8):1552-61.
Score: 0.027
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QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications. Clin Imaging. 2021 Sep; 77:151-157.
Score: 0.026
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Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility. J Digit Imaging. 1999 Feb; 12(1):34-42.
Score: 0.023
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Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
Score: 0.017
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Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys. 2003 Jul; 30(7):1602-17.
Score: 0.008