Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Author:

Mitchell Joseph Ross1,Kamnitsas Konstantinos2,Singleton Kyle W.3,Whitmire Scott A.3,Clark-Swanson Kamala R.3,Ranjbar Sara3,Rickertsen Cassandra R.3,Johnston Sandra K.3,Egan Kathleen M.4,Rollison Dana E.4,Arrington John5,Krecke Karl N.6,Passe Theodore J.6,Verdoorn Jared T.6,Nagelschneider Alex A.6,Carr Carrie M.6,Port John D.6,Patton Alice6,Campeau Norbert G.6,Liebo Greta B.6,Eckel Laurence J.6,Wood Christopher P.6,Hunt Christopher H.6,Vibhute Prasanna6,Nelson Kent D.6,Hoxworth Joseph M.6,Patel Ameet C.6,Chong Brian W.6,Ross Jeffrey S.6,Boxerman Jerrold L.7,Vogelbaum Michael A.8,Hu Leland S.3,Glocker Ben2,Swanson Kristin R.3

Affiliation:

1. H. Lee Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida

2. Imperial College, Biomedical Image Analysis Group, London

3. Mayo Clinic, Mathematical NeuroOncology Lab, Phoenix, Arizona

4. H. Lee Moffitt Cancer Center and Research Institute, Department of Cancer Epidemiology, Tampa, Flori

5. H. Lee Moffitt Cancer Center and Research Institute, Department of Diagnostic Imaging and Interventi

6. Mayo Clinic, Department of Radiology, Rochester, Minnesota

7. Rhode Island Hospital and Alpert Medical School of Brown University, Department of Diagnostic Imagin

8. H. Lee Moffitt Cancer Center and Research Institute, Department of Neurosurgery, Tampa, Florida

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

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