Improving labeling efficiency in automatic quality control of MRSI data.

Da Silva Mendes Pedrosa, Nuno Miguel; McKinley, Richard; Wiest, Roland; Slotboom, Johannes (2017). Improving labeling efficiency in automatic quality control of MRSI data. Magnetic resonance in medicine, 78(6), pp. 2399-2405. Wiley-Liss 10.1002/mrm.26618

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PURPOSE 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. CONCLUSION Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Da Silva Mendes Pedrosa, Nuno Miguel; McKinley, Richard; Wiest, Roland and Slotboom, Johannes

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0740-3194

Publisher:

Wiley-Liss

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

28 Jul 2017 08:46

Last Modified:

15 Nov 2017 01:30

Publisher DOI:

10.1002/mrm.26618

PubMed ID:

28169457

Uncontrolled Keywords:

MRSI; active learning; artifact detection; labeling efficiency; machine learning; quality control

BORIS DOI:

10.7892/boris.95960

URI:

https://boris.unibe.ch/id/eprint/95960

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