Comparison of supervised MRI segmentation methods for tumor volume determination during therapy |
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| Institution: | 1. Department of Radiology, University of South Florida, Tampa, FL 33612, USA;2. H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;3. Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33647, USA;4. Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA;5. Neuro-oncology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;6. Radiation-oncology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;1. Nikolaev Institute of Inorganic Chemistry SB RAS, Lavrentyev Av. 3, 630090 Novosibirsk, Russia;2. Boreskov Institute of Catalysis SB RAS, Lavrentyev Av. 5, 630090 Novosibirsk, Russia;1. School of Mechanical and Chemical Engineering M050, The University of Western Australia, 35 Stirling Highway, Crawley 6009, Western Australia, Australia;2. Department of Petroleum Engineering, Curtin University, 26 Dick Perry Avenue, 6151 Kensington, Australia;3. Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, United Kingdom;1. Pacific Northwest National Laboratory, Richland, WA, 99354, USA;2. School of Earth Sciences, The Ohio State University, Columbus, OH, 43210, USA;1. Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;2. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;3. Neil and Elise Wallace STRATUS Center for Medical Simulation, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;4. Children’s Hospital, Harvard Medical School, Boston, MA, USA;5. Department of Orthopedics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;6. University of Arizona Arthritis Center, The University of Arizona College of Medicine, Tucson, AZ, USA;1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China;2. Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China;3. Department of Obstetrics and Gynecology, Tianjin University Hospital, Tianjin 300072, China;4. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China;5. Department of Radiology, The Second Hospital of Hebei Medical University, 215 Hepingxi Road, Shijiazhuang, Heibei Province, China |
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| Abstract: | Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T1-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T1-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements. |
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