Multiparametric medical imaging data can be large and are often complex. Machine learning algorithms can assist in image interpretation when reliable training data exist. In most cases, however, knowledge about ground truth (e.g. histology) and thus training data is limited, which makes application of machine learning algorithms difficult.
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The purpose of this study was to design and implement a learning algorithm for classification of multidimensional medical imaging data that is robust and accurate even with limited prior knowledge and that allows for generalization and application to unseen data. Local prostate cancer was chosen as a model for application and validation. 16 patients underwent combined simultaneous [(11) C]-choline positron emission tomography (PET)/MRI.
The following imaging parameters were acquired: T2 signal intensities, apparent diffusion coefficients, parameters Ktrans and Kep from dynamic contrast-enhanced MRI, and PET standardized uptake values (SUVs). A spatially constrained fuzzy c-means algorithm (sFCM) was applied to the single datasets and the resulting labeled data were used for training of a support vector machine (SVM) classifier. Accuracy and false positive and false negative rates of the proposed algorithm were determined in comparison with manual tumor delineation. For five of the 16 patients rates were also determined in comparison with the histopathological standard of reference.
The combined sFCM/SVM algorithm proposed in this study revealed reliable classification results consistent with the histopathological reference standard and comparable to those of manual tumor delineation. sFCM/SVM generally performed better than unsupervised sFCM alone. We observed an improvement in accuracy with increasing number of imaging parameters used for clustering and SVM training. In particular, including PET SUVs as an additional parameter markedly improved classification results.
A variety of applications are conceivable, especially for imaging of tissues without easily available histopathological correlation.
Copyright © 2015 John Wiley & Sons, Ltd.
NMR Biomed. 2015 Jul;28(7):914-22. doi: 10.1002/nbm.3329. Epub 2015 May 26.
Gatidis S1, Scharpf M2, Martirosian P1, Bezrukov I3,4, Küstner T1, Hennenlotter J5, Kruck S5, Kaufmann S1, Schraml C1, la Fougère C6, Schwenzer NF1, Schmidt H1.
1 Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
2 Department of Pathology and Neuropathology, General Pathology, Eberhard Karls University Tübingen, Germany.
3 Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany.
4 Department of Radiology, Preclinical Imaging and Radiopharmacy, Laboratory for Preclinical Imaging and Imaging Technology of the Werner Siemens Foundation, Eberhard Karls University Tübingen, Germany.
5 Department of Urology, Eberhard Karls University Tübingen, Germany.
6 Department of Radiology, Nuclear Medicine, Eberhard Karls University Tübingen, Germany.