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 78:2399-2405, 2017.
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 tumorpatients 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 78:2399-2405, 2017.
Authors: Saumya S Gurbani; Eduard Schreibmann; Andrew A Maudsley; James Scott Cordova; Brian J Soher; Harish Poptani; Gaurav Verma; Peter B Barker; Hyunsuk Shim; Lee A D Cooper Journal: Magn Reson Med Date: 2018-03-09 Impact factor: 4.668
Authors: Saumya S Gurbani; Sulaiman Sheriff; Andrew A Maudsley; Hyunsuk Shim; Lee A D Cooper Journal: Magn Reson Med Date: 2019-01-21 Impact factor: 4.668