People

Samuel G. Armato, PhD

  • Associate Professor of Radiology
    Committee Chair of Committee on Medical Physics
  • Research and Scholarly Interests: chest radiography, computed tomography (CT), computer vision, computer-aided diagnosis, imaging physics, lung cancer, lung imaging, medical image analysis, medical physics, mesothelioma
  • Websites: Research Network Profile
  • Contact: sarmato@uchicago.edu
  • Graduate Program: Medical Physics

I have established my career through the development and evaluation of computerized techniques for the quantitative analysis of medical images and the assessment of tumor response to therapy. More specifically, my research has involved the computerized detection and evaluation of lung nodules in thoracic computed tomography (CT) scans, the assessment of image quality and pathologic change in temporally subtracted chest radiographic images, the computerized evaluation of mesothelioma tumor and response to therapy in CT scans, critical analyses of image-based tumor response assessment for mesothelioma, the development of objective CT-based metrics for the quantification of mucosal inflammation due to sinusitis, the application of radiomics to the pre- and post-treatment CT scans of radiation therapy patients to predict normal lung tissue complications, and the evaluation of reference standards for computer-aided diagnosis (CAD) research. As the local principal investigator for the Lung Image Database Consortium project that spanned 10 years and as the faculty director of the University of Chicago’s Human Imaging Research Office, I have extensive experience with interdisciplinary and multi-institutional image-based projects.

The University of Chicago
Chicago, IL
Ph.D. - Medical Physics
1997

The University of Chicago
Chicago, IL
B.A. - Physics
1987

Radiomics-based assessment of idiopathic pulmonary fibrosis is associated with genetic mutations and patient survival.
Budzikowski JD, Foy JJ, Rashid AA, Chung JH, Noth I, Armato SG. Radiomics-based assessment of idiopathic pulmonary fibrosis is associated with genetic mutations and patient survival. J Med Imaging (Bellingham). 2021 May; 8(3):031903.
PMID: 33889657

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications.
Avila RS, Fain SB, Hatt C, Armato SG, Mulshine JL, Gierada D, Silva M, Lynch DA, Hoffman EA, Ranallo FN, Mayo JR, Yankelevitz D, Estepar RSJ, Subramaniam R, Henschke CI, Guimaraes A, Sullivan DC. QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications. Clin Imaging. 2021 Feb 25; 77:151-157.
PMID: 33684789

Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.
Foy JJ, Shenouda M, Ramahi S, Armato S, Ginat DT. Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features. J Med Imaging (Bellingham). 2020 Nov; 7(6):064007.
PMID: 33409336

Deep Learning Demonstrates Potential for Lung Cancer Detection in Chest Radiography.
Armato SG. Deep Learning Demonstrates Potential for Lung Cancer Detection in Chest Radiography. Radiology. 2020 12; 297(3):697-698.
PMID: 32965172

Ontology-Based Radiology Teaching File Summarization, Coverage, and Integration.
Deshpande P, Rasin A, Son J, Kim S, Brown E, Furst J, Raicu DS, Montner SM, Armato SG. Ontology-Based Radiology Teaching File Summarization, Coverage, and Integration. J Digit Imaging. 2020 06; 33(3):797-813.
PMID: 32253657

Biomedical image analysis challenges should be considered as an academic exercise, not an instrument that will move the field forward in a real, practical way.
Armato SG, Farahani K, Zaidi H. Biomedical image analysis challenges should be considered as an academic exercise, not an instrument that will move the field forward in a real, practical way. Med Phys. 2020 Jun; 47(6):2325-2328.
PMID: 32040865

Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis.
Foy JJ, Armato SG, Al-Hallaq HA. Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis. J Med Imaging (Bellingham). 2020 Jan; 7(1):014504.
PMID: 32118090

Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion.
Gudmundsson E, Straus CM, Li F, Armato SG. Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion. J Med Imaging (Bellingham). 2020 Jan; 7(1):012705.
PMID: 32016133

Optimization of response classification criteria for patients with malignant pleural mesothelioma, a validation study.
Buikhuisen WA, Qayyum F, Armato SG, Baas P. Optimization of response classification criteria for patients with malignant pleural mesothelioma, a validation study. Lung Cancer. 2019 12; 138:139-140.
PMID: 31733933

Response.
MacMahon H, Li F, Jiang Y, Armato SG. Response. Chest. 2019 10; 156(4):810-811.
PMID: 31590714

View All Publications

Fellow
Society of Photo-Optical Instrumentation Engineers (SPIE)
2018

Distinguished Investigator
Academy of Radiology Research
2016

Fellow
American Association of Physicists in Medicine
2014

Kurt Rossmann Award for Excellence in Medical Physics Teaching
The University of Chicago
2012

Raine Visiting Professor
University of Western Australia
2009

Kurt Rossmann Award for Excellence in Medical Physics Teaching
The University of Chicago
2002