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An atlas of classifiers—a machine learning paradigm for brain MRI segmentation

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Abstract

We present the Atlas of Classifiers (AoC)—a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients.

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Notes

  1. Estimation of tissues’ intensities using normal distributions is subject to the removal of the bias field either before or during the segmentation process.

  2. MRBrainS13 challenge: https://mrbrains13.isi.uu.nl/results/

  3. SPM package:http://www.fil.ion.ucl.ac.uk/spm

  4. This data was provided by the challenge organizers upon our request.

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Acknowledgements

We would also like to thank the MRBrainS13 organizers for their help with the cross-modality evaluation on the MRBrainS13 test set. Specifically we would like to thank Adrienne Mendrik and Edwin Bennink for running the evaluation.

Funding

This study was partially supported by the Israel Science Foundation (1638/16, T.R.R) and by the the Israeli Ministry of Science & Technology (J.G.).

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Authors and Affiliations

  1. The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Shiri Gordon, Boris Kodner, Tal Goldfryd, Michael Sidorov & Tammy Riklin Raviv

  2. The Faculty of Electrical Engineering, Ber-Ilan University, Ramat-Gan, Israel

    Jacob Goldberger

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  1. Shiri Gordon
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  2. Boris Kodner
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  3. Tal Goldfryd
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Correspondence to Tammy Riklin Raviv.

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Gordon, S., Kodner, B., Goldfryd, T. et al. An atlas of classifiers—a machine learning paradigm for brain MRI segmentation. Med Biol Eng Comput 59, 1833–1849 (2021). https://doi.org/10.1007/s11517-021-02414-x

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