Medicine and Health Sciences Commons^™

Interactive Machine Learning-Based Multi-Label Segmentation Of Solid Tumors And Organs, Dimitrios Bounias, Ashish Singh, Spyridon Bakas, Sarthak Pati, Saima Rathore, Hamed Akbari, Michel Bilello, Benjamin Greenberger, Joseph Lombardo, Rhea Chitalia, Nariman Jahani, Aimilia Gastounioti, Michelle Hershman, Leonid Roshkovan, Sharyn Katz, Bardia Yousefi, Carolyn Lou, Amber Simpson, Richard Do, Russell Shinohara, Despina Kontos, Konstantina Nikita, Christos Davatzikos

Department of Radiation Oncology Faculty Papers

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% …