An easy-to-use image labeling platform for automatic magnetic resonance image quality assessment

In medical imaging, images are usually evaluated by a human observer (HO) depending on the underlying diagnostic question which can be a time-demanding and cost-intensive process. Model observers (MO) which mimic the human visual system can help to support the HO during this reading process or can provide feedback to the MR scanner and/or HO about the derived image quality. For this purpose MOs are trained on HO-derived image labels with respect to a certain diagnostic task. We propose a non-reference image quality assessment system based on a machine-learning approach with a deep neural network and active learning to keep the amount of needed labeled training data small. A labeling platform is developed as a web application with accounted data security and confidentiality to facilitate the HO labeling procedure. The platform is made publicly available.

[1]  Jovan G. Brankov,et al.  Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images , 2014, IEEE Transactions on Medical Imaging.

[2]  William A. Gale,et al.  A sequential algorithm for training text classifiers , 1994, SIGIR '94.

[3]  Mahdi M. Kalayeh,et al.  Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment , 2013, IEEE Transactions on Nuclear Science.

[4]  Yongyi Yang,et al.  Learning a Channelized Observer for Image Quality Assessment , 2009, IEEE Transactions on Medical Imaging.

[5]  W. Geisler.  Visual perception and the statistical properties of natural scenes. , 2008, Annual review of psychology.

[6]  Donglai Huo,et al.  Methods for quantitative image quality evaluation of MRI parallel reconstructions: detection and perceptual difference model. , 2007, Magnetic resonance imaging.

[7]  Luis Ibáñez,et al.  The Design of SimpleITK , 2013, Front. Neuroinform..

[8]  Bin Yang,et al.  Active learning for magnetic resonance image quality assessment , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  M.N. Wernick,et al.  Learning a nonlinear channelized observer for image quality assessment , 2003, 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).

[10]  H.H. Barrett,et al.  Model observers for assessment of image quality , 1993, 2002 IEEE Nuclear Science Symposium Conference Record.

[11]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[12]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[13]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[14]  Mark Craven,et al.  An Analysis of Active Learning Strategies for Sequence Labeling Tasks , 2008, EMNLP.

[15]  Patrick Le Callet,et al.  A multi-slice model observer for medical image quality assessment , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Jovan G. Brankov,et al.  Active learning for image quality assessment by model observer , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[17]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..