alphagui / InteractiveSegmentation

MITK Plugin for Interactive Segmentation

Interactive Machine Learning-based Multi-label Segmentation of Solid Tumors and Organs

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Dimitrios Bounias (1,2,3) Ashish Singh (1,2) Spyridon Bakas, PhD (1,2,4) Sarthak Pati (1,2) Saima Rathore, PhD (1,2) Hamed Akbari, MD, PhD (1,2) Michel Bilello, MD, PhD (1,2) Benjamin A. Greenberger, MD (7,1,2) Joseph Lombardo, DO, PharmD (7) Rhea D. Chitalia (1,2,8) Nariman Jahani, PhD (1,2,8) Aimilia Gastounioti, PhD (1,2,8) Michelle Hershman, MD (9) Leonid Roshkovan, MD (2) Sharyn I. Katz, MD (2) Bardia Yousefi, PhD (1,2,8) Carolyn Lou (1,5,6) Amber L. Simpson PhD (10) Russell T. Shinohara, PhD (1,5,6) Despina Kontos, PhD (1,2,8) Konstantina Nikita, MD, PhD (3) Christos Davatzikos, PhD (1,2)

1. Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA

2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

4. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

5. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

6. Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology (PennSIVE), and Informatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, USA

7. Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center at Thomas Jefferson University, Philadelphia, PA

8. Computational Breast Imaging Group (CBIG), University of Pennsylvania, Philadelphia, PA, USA

9. Banner University Medical Center Tucson, Arizona, AZ

10. School of Computing and Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada

Summary

The proposed IML-based segmentation method offers the capability to derive patient-specific machine learning models, resulting in annotations comparable to, and in some cases better than, expert-drawn manual annotations, both in terms of accuracy and consistency, while offering a significant gain in speed. Quantitative evaluation in multiple anatomical structures supports this method’s generalizability and promise for real-world application, facilitating faster generation of expert annotations