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Ronald Summers

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Ronald M. Summers
CitizenshipUSA
Alma materUniversity of Pennsylvania
Known forCT Colonography, deep learning in radiology
Scientific career
InstitutionsNIH

Ronald Marc Summers is an American radiologist and senior investigator at the Diagnostic Radiology Department at the NIH Clinical Center in Bethesda, Maryland. He is chief of the Clinical Image Processing Service and directs the Imaging Biomarkers and Computer-Aided Diagnosis (CAD) Laboratory. A researcher in the field of radiology and computer-aided diagnosis, he has co-authored over 500 journal articles and conference proceedings papers and is a coinventor on 12 patents.[1] His lab has conducted research applying artificial intelligence and deep learning to radiology.[2][3][4]

Background

Summers received his B.A. degree in physics from the University of Pennsylvania in 1981, where he also obtained his M.D. and Ph.D. degrees in Medicine/Anatomy & Cell Biology in 1988.[5] He completed a medical internship at the Penn Presbyterian Medical Center in Philadelphia, Pennsylvania, a radiology residency at the University of Michigan, Ann Arbor, MI (1989–1993) and an MRI fellowship at Duke University, Durham, NC (1993–1994).[6]

Research

Summers' lab is known for developing software for "virtual colonoscopy" and computer aided detection (CAD) algorithms which assist in the detection of colon polyps.[7] His lab is also known for multi-organ multi-atlas registration and the development of large radiologic image databases. Summers is also a practicing clinician – his clinical areas of specialty are thoracic and gastrointestinal radiology and body cross-sectional imaging.[6]

Summers' lab is known for pioneering work in the application of deep learning to problems in medical imaging such as computer aided detection, classification, and segmentation. A February 2016 paper from his lab exploring convolutional neural network architectures and transfer learning for lymph node detection and interstitial lung disease classification had over 1,000 citations as of early 2019.[8] In 2018 he was the keynote speaker at the inaugural Medical Imaging and Deep Learning (MIDL) conference.[9]

In September 2017 his lab released 100,000 anonymized chest x-ray images from 30,000 patients, including many with advanced lung disease.[10][11]

In July 2018, his lab released DeepLesion, a dataset of 32,000 annotated lesions identified on CT images spread over 4,400 patients.[12][13][14][15] At the 2019 IEEE Symposium on Biomedical Imaging (ISBI) Youbao Tang, a postdoc in Summers' lab, unveiled a universal lesion detector (nicknamed "ULDor") which uses a mask R-CNN architecture to detect many types of lesions throughout the body with high precision.[16]

In 2019 his lab has demonstrated how to generate weak labels from clinically generated medical reports using deep learning and natural language processing techniques, thus greatly reducing the need for burdensome hand annotation of datasets.[17]

Summers and collaborators have also developed a tool for opportunistic fully automated bone mineral density (BMD) measurement in CT scans which has been used to track BMD changes in large longitudinal cohorts.[18][19] Together with Perry Pickhardt and collaborators, the tool was used to track bone mineral density changes in 20,000 subjects.[20][21] Summers' lab has also demonstrated the utility of deep learning for performing automated measurement of muscle,[22] liver fat,[23] vertebral levels,[24] and plaque in large datasets.[25] A 2022 paper from Summers' lab published in Radiology showed how computed tomography (CT) biomarkers are associated with diabetes and pre-diabetes.[26][27]

Summers has served as a member of the editorial boards of the journals Radiology: Artificial Intelligence, Journal of Medical Imaging, and Academic Radiology and is a Fellow of the Society of Abdominal Radiologists and the American Institute for Medical and Biological Engineering (AIMBE).[6]

Awards

References

  1. ^ "Ronald M. Summers, MD, PhD". scholar.google.com. Google Scholar Citations. Retrieved 21 December 2018.
  2. ^ Pearson, Dave (1 July 2016). "Radiologists sharing more abdominal duties with computers". Health Imaging. Retrieved 22 December 2018.
  3. ^ "Doctor Data: How Computers Are Invading the Clinic". NIH Intramural Research Program. 2 August 2018. Retrieved 22 December 2018.
  4. ^ "Share Your Science: The Impact of Deep Learning on Radiology". NVIDIA Developer News Center. 13 December 2016. Retrieved 21 December 2018.
  5. ^ "NIH Clinical Center: Curriculum Vitae for Ronald M. Summers, MD, PhD". www.cc.nih.gov. Retrieved 21 December 2018.
  6. ^ a b c "NIH Clinical Center Senior Staff". NIH Clinical Center. Retrieved 24 December 2018. Public Domain This article incorporates text from this source, which is in the public domain.
  7. ^ Summers, Ronald M.; Yao, Jianhua; Pickhardt, Perry J.; Franaszek, Marek; Bitter, Ingmar; Brickman, Daniel; Krishna, Vamsi; Choi, J. Richard (December 2005). "Computed Tomographic Virtual Colonoscopy Computer-Aided Polyp Detection in a Screening Population". Gastroenterology. 129 (6): 1832–1844. doi:10.1053/j.gastro.2005.08.054. PMC 1576342. PMID 16344052.
  8. ^ Shin, Hoo-Chang; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M. (May 2016). "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning". IEEE Transactions on Medical Imaging. 35 (5): 1285–1298. arXiv:1602.03409. Bibcode:2016arXiv160203409S. doi:10.1109/TMI.2016.2528162. PMC 4890616. PMID 26886976.
  9. ^ "MIDL2018, Day 1: Keynote by Prof. Ronald Summers". YouTube. Retrieved 22 December 2018.
  10. ^ "NIH Clinical Center provides one of the largest publicly available chest x-ray datasets to scientific community". National Institutes of Health (NIH). 27 September 2017. Retrieved 22 December 2018.
  11. ^ Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR 2017
  12. ^ "NIH Clinical Center releases dataset of 32,000 CT images". National Institutes of Health (NIH). 20 July 2018. Retrieved 22 December 2018.
  13. ^ "DeepLesion dataset". Retrieved 22 December 2018.
  14. ^ Yan, Ke; Wang, Xiaosong; Lu, Le; Summers, Ronald M. (20 July 2018). "DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning". Journal of Medical Imaging. 5 (3): 036501. doi:10.1117/1.JMI.5.3.036501. PMC 6052252. PMID 30035154.
  15. ^ Summers, Ronald M.; Bagheri, Mohammad Hadi; Harrison, Adam P.; Zhang, Ling; Lu, Le; Wang, Xiaosong; Yan, Ke (2018). "Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database": 9261–9270. {{cite journal}}: Cite journal requires |journal= (help)
  16. ^ Summers, Ronald M.; Xiao, Jing; Liu, Jiamin; Tang, Yuxing; Yan, Ke; Tang, Youbao (18 January 2019). "ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining". arXiv:1901.06359. Bibcode:2019arXiv190106359T. {{cite journal}}: Cite journal requires |journal= (help)
  17. ^ Summers, Ronald M.; Lu, Zhiyong; Bagheri, Mohammadhadi; Sandfort, Veit; Peng, Yifan; Yan, Ke (9 April 2019). "Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology". arXiv:1904.04661. Bibcode:2019arXiv190404661Y. {{cite journal}}: Cite journal requires |journal= (help)
  18. ^ Summers, Ronald M.; Baecher, Nicolai; Yao, Jianhua; Liu, Jiamin; Pickhardt, Perry J.; Choi, J. Richard; Hill, Suvimol (March 2011). "Feasibility of Simultaneous Computed Tomographic Colonography and Fully Automated Bone Mineral Densitometry in a Single Examination". Journal of Computer Assisted Tomography. 35 (2): 212–216. doi:10.1097/RCT.0b013e3182032537. PMC 3077119. PMID 21412092.
  19. ^ Pickhardt, Perry J.; Lee, Scott J.; Liu, Jiamin; Yao, Jianhua; Lay, Nathan; Graffy, Peter M; Summers, Ronald M (February 2019). "Population-based opportunistic osteoporosis screening: Validation of a fully automated CT tool for assessing longitudinal BMD changes". The British Journal of Radiology. 92 (1094): 20180726. doi:10.1259/bjr.20180726. PMC 6404831. PMID 30433815.
  20. ^ Pearson, Dave (28 March 2019). "Opportunity emerges for osteoporosis screening via routine CT". Health Imaging. Retrieved 15 June 2019.
  21. ^ Jang, Samuel; Graffy, Peter M.; Ziemlewicz, Timothy J.; Lee, Scott J.; Summers, Ronald M.; Pickhardt, Perry J. (May 2019). "Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20 000 Adults". Radiology. 291 (2): 360–367. doi:10.1148/radiol.2019181648. PMC 6492986. PMID 30912719.
  22. ^ Burns, Joseph E.; Yao, Jianhua; Chalhoub, Didier; Chen, Joseph J.; Summers, Ronald M. (March 2020). "A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT". Academic Radiology. 27 (3): 311–320. doi:10.1016/j.acra.2019.03.011. PMID 31126808. S2CID 164219063.
  23. ^ Graffy, Peter M.; Sandfort, Veit; Summers, Ronald M.; Pickhardt, Perry J. (November 2019). "Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment". Radiology. 293 (2): 334–342. doi:10.1148/radiol.2019190512. PMC 6822771. PMID 31526254.
  24. ^ Elton, Daniel; Sandfort, Veit; Pickhardt, Perry J.; Summers, Ronald M. (16 March 2020). "Accurately identifying vertebral levels in large datasets". In Hahn, Horst K; Mazurowski, Maciej A (eds.). Medical Imaging 2020: Computer-Aided Diagnosis. SPIE. p. 23. arXiv:2001.10503. doi:10.1117/12.2551247. ISBN 9781510633957. S2CID 210932251. {{cite book}}: |website= ignored (help)
  25. ^ Pickhardt, Perry J; Graffy, Peter M; Zea, Ryan; Lee, Scott J; Liu, Jiamin; Sandfort, Veit; Summers, Ronald M (April 2020). "Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study". The Lancet Digital Health. 2 (4): e192–e200. doi:10.1016/S2589-7500(20)30025-X. PMC 7454161. PMID 32864598.
  26. ^ Tallam, Hima; Elton, Daniel C.; Lee, Sungwon; Wakim, Paul; Pickhardt, Perry J.; Summers, Ronald M. (July 2022). "Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning". Radiology. 304 (1): 85–95. doi:10.1148/radiol.211914. PMC 9270681. PMID 35380492.
  27. ^ "Artificial intelligence may improve diabetes diagnosis". EurekAlert!. Retrieved 15 February 2023.
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