Improving labeling efficiency in automatic quality control of MRSI data.
OBJECTIVE
To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data.


METHODS
28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling.


RESULTS
The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance.


CONCLUSIONS
Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
Support Center for Advanced Neuroimaging, Inselspital, Bern, Switzerland.|University of Bern, Bern, Switzerland.
Nuno
N
Pedrosa de Barros
Support Center for Advanced Neuroimaging, Inselspital, Bern, Switzerland.|University of Bern, Bern, Switzerland.
Richard
R
McKinley
Support Center for Advanced Neuroimaging, Inselspital, Bern, Switzerland.|University of Bern, Bern, Switzerland.
Roland
R
Wiest
Support Center for Advanced Neuroimaging, Inselspital, Bern, Switzerland.|University of Bern, Bern, Switzerland.
Johannes
J
Slotboom
D000465:Algorithms; D019540:Area Under Curve; D016477:Artifacts; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009682:Magnetic Resonance Spectroscopy; D015233:Models, Statistical; D011786:Quality Control; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio
MRSI; active learning; artifact detection; labeling efficiency; machine learning; quality control