Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

Medical Image Analysis

Volumn 24, Issue 1, 2015, Pages 245-254

  Opbroek, Annegreet van ^a   Vernooij, Meike W ^a   Ikram, M Arfan ^a   de Bruijne, Marleen ^a,b  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Brain; Domain adaptation; Machine learning; MRI; Transfer learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BRAIN; CLASSIFICATION (OF INFORMATION); IMAGE SEGMENTATION; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; PROBABILITY DENSITY FUNCTION; SCANNING; SUPERVISED LEARNING; TISSUE;

AUTOMATIC SEGMENTATIONS; BRAIN TISSUE SEGMENTATIONS; CLASSIFICATION ERRORS; DOMAIN ADAPTATION; SUPERVISED MACHINE LEARNING; SUPERVISED SEGMENTATION; TRADITIONAL APPROACHES; TRANSFER LEARNING;

IMAGE CLASSIFICATION;

ARTICLE; BRAIN TISSUE SEGMENTATION; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL ARTICLE; CONTROLLED STUDY; HUMAN; HUMAN TISSUE; IMAGING; IMAGING AND DISPLAY; MATHEMATICAL PARAMETERS; MULTI FEATURE VOXELWISE CLASSIFICATION; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; PROBABILITY DENSITY FUNCTION; SKULL STRIPPING; SUPPORT VECTOR MACHINE; WEIGHTING TRAINING IMAGE; WHITE MATTER LESION SEGMENTATION; ALGORITHM; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; SUPERVISED MACHINE LEARNING;

ALGORITHMS; BRAIN; DATA INTERPRETATION, STATISTICAL; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STATISTICAL DISTRIBUTIONS; SUBTRACTION TECHNIQUE; SUPERVISED MACHINE LEARNING;

EID: 84939458403     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2015.06.010     Document Type: Article

References (37)

1. Ablavsky, V., Becker, C., Fua, P., 2012. Transfer Learning by Sharing Support Vectors. No. EPFL-Report-181360.
2. Anbeek P., Vincken K., Van Bochove G., Van Osch M., Van der Grond J., et al. Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 2005, 27(4):795.
3. Ashburner J., Friston K. Unified segmentation. Neuroimage 2005, 26(3):839-851.
4. Becker C., Christoudias C., Fua P. Domain adaptation for microscopy imaging. IEEE Trans. Med. Imaging 2015, 34(5):1125.
5. Cha S. Comprehensive survey on distance/similarity measures between probability density functions. City 2007, 1(2):1.
6. Chang C., Lin C. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol 2011, 2(3):27.
7. Cheng B., Zhang D., Jie B., Shen D. Sparse multimodal manifold-regularized transfer learning for MCI conversion prediction. Machine Learning in Medical Imaging 2013, 251-259. Springer.
8. Cheng B., Zhang D., Shen D. Domain transfer learning for MCI conversion prediction. Proceeedings of the Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, 82-90. Springer.
9. Coleman T., Li Y. An interior trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim. 1996, 6(2):418-445.
10. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20(3):273-297.
11. Fan W., Davidson I., Zadrozny B., Yu P. An improved categorization of classifier's sensitivity on sample selection bias. Proceedings of the Fifth IEEE International Conference on Data Mining 2005, 4-pp.
12. Folkesson J., Dam E., Olsen O., Pettersen P., Christiansen C. Segmenting articular cartilage automatically using a voxel classification approach. IEEE Trans. Med. Imaging 2007, 26(1):106-115.
13. Geras K., Sutton C. Multiple-source cross-validation. Proceedings of the 30th International Conference on Machine Learning, ICML-13 and Proceedings of JMLR Workshop and Conference 2013, 1292-1300.
14. Geremia E., Clatz O., Menze B., Konukoglu E., Criminisi A., Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 2011, 57(2):378-390.
15. Goetz M., Weber C., Stieltjes B., Maier-Hein K. Learning from small amounts of labeled data in a brain tumor classification task. Proceedings of Neural Information Processing Systems, NIPS 2014.
16. Guerrero R., Ledig C., Rueckert D. Manifold alignment and transfer learning for classification of alzheimers disease. Machine Learning in Medical Imaging 2014, 77-84. Springer.
17. Heimann T., Mountney P., John M., Ionasec R. Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data. Med. Image anal. 2014, 18(8):1320-1328.
18. Huang J., Gretto A., B.K.M., Schölkopf B., Smola A. Correcting sample selection bias by unlabeled data. Proceedings of Advances in neural information processing systems, NIPS 2006, 601-608.
19. Ikram M., Van der Lugt A., Niessen W., Krestin G., Koudstaal P., Hofman A., Breteler M., Vernooij M. The Rotterdam scan study: design and update up to 2012. Eur. J. Epidemiol. 2011, 26(10):811-824.
20. Van der Lijn F., Den Heijer T., Breteler M., Niessen W. Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. Neuroimage 2008, 43(4):708-720.
21. Liu F., Xu D., Ferguson M., Chu B., Saam T., Takaya N., Hatsukami T., Yuan C., Kerwin W. Automated in vivo segmentation of carotid plaque MRI with morphology-enhanced probability maps. Magn. Reson. Med. 2006, 55(3):659-668.
22. Pan S., Yang Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 2010, 22(10):1345-1359.
23. Patel V., Gopalan R., Li R., Chellappa R. Visual domain adaptation: a survey of recent advances. IEEE Signal Process. Mag. 2015, 32(3):53-69.
24. Scholkopf B., Smola A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2001, MIT press.
25. Silverman B. Density Estimation for Statistics and Data Analysis 1986, vol. 26. Chapman & Hall/CRC.
26. Smith S. Fast robust automated brain extraction. Hum. Brain Mapp. 2002, 17(3):143-155.
27. Styner M., Lee J., Chin B., Chin M., Commowick O., Tran H., Markovic-Plese S., Jewells V., Warfield S. 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation. MIDAS J. 2008, 1-5.
28. Sugiyama M., Nakajima S., Kashima H., Von Buenau P., Kawanabe M. Direct importance estimation with model selection and its application to covariate shift adaptation. Adv. Neural Inf. Process. Sys. 2008, 20:1433-1440.
29. Sugiyama M., Suzuki T., Kanamori T. Density ratio estimation: a comprehensive review. RIMS Kokyuroku 2010, 1703:10-31.
30. Tustison N., Avants B., Cook P., Zheng Y., Egan A., Yushkevich P., Gee J. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 2010, 29(6):1310-1320.
31. Van Engelen A., van Dijk A., Truijman M., Van T Klooster R., Van Opbroek A., Van der Lugt A., Niessen W., Kooi M., De Bruijne M. Multi-center MRI carotid plaque component segmentation using feature normalization and transfer learning. IEEE Trans. Med. Imaging 2015, 34(6):1294.
32. Van Opbroek A., Ikram M., Vernooij M., de Bruijne M. Transfer learning improves supervised image segmentation across imaging protocols. IEEE Trans. Med. Imaging 2015, 34(5):1018.
33. Van Opbroek A., Ikram M., Vernooij M., De Bruijne M. Supervised image segmentation across scanner protocols: a transfer learning approach. Mach. Learn. Med. Imaging 2012, 7588:160-167.
34. Van Opbroek A., Ikram M., Vernooij M., De Bruijne M. A transfer-learning approach to image segmentation across scanners by maximizing distribution similarity. Machine Learning in Medical Imaging 2013, 49-56. Springer.
35. Van Opbroek A., Van der Lijn F., De Bruijne M. Automated brain-tissue segmentation by multi-feature SVM classification. Proceedings of the Workshop on Grand Challenge on MR Brain Image Segmentation, MRBRainS13 2013.
36. Worth, A., 2009. The Internet Brain Segmentation Repository, IBSR. http://www.cma.mgh.harvard.edu/ibsr.
37. Zadrozny B. Learning and evaluating classifiers under sample selection bias. Proceedings of the Twenty-First International Conference on Machine learning, ACM 2004, 114.