MRI bias field correction with an implicitly trained CNN

Attila Simkó, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:1125-1138, 2022.

Abstract

In magnetic resonance imaging (MRI), bias fields are difficult to correct since they are inherently unknown. They cause intra-volume intensity inhomogeneities which limit the performance of subsequent automatic medical imaging tasks, \eg, tissue-based segmentation. Since the ground truth is unavailable, training a supervised machine learning solution requires approximating the bias fields, which limits the resulting method. We introduce implicit training which sidesteps the inherent lack of data and allows the training of machine learning solutions without ground truth. We describe how training a model implicitly for bias field correction allows using non-medical data for training, achieving a highly generalized model. The implicit approach was compared to a more traditional training based on medical data. Both models were compared to an optimized N4ITK method, with evaluations on six datasets. The implicitly trained model improved the homogeneity of all encountered medical data, and it generalized better for a range of anatomies, than the model trained traditionally. The model achieves a significant speed-up over an optimized N4ITK method—by a factor of $100$, and after training, it also requires no parameters to tune. For tasks such as bias field correction—where ground truth is generally not available, but the characteristics of the corruption are known—implicit training promises to be a fruitful alternative for highly generalized solutions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-simko22a, title = {MRI bias field correction with an implicitly trained CNN}, author = {Simk{\'{o}}, Attila and L{\"{o}}fstedt, Tommy and Garpebring, Anders and Nyholm, Tufve and Jonsson, Joakim}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {1125--1138}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/simko22a/simko22a.pdf}, url = {https://proceedings.mlr.press/v172/simko22a.html}, abstract = {In magnetic resonance imaging (MRI), bias fields are difficult to correct since they are inherently unknown. They cause intra-volume intensity inhomogeneities which limit the performance of subsequent automatic medical imaging tasks, \eg, tissue-based segmentation. Since the ground truth is unavailable, training a supervised machine learning solution requires approximating the bias fields, which limits the resulting method. We introduce implicit training which sidesteps the inherent lack of data and allows the training of machine learning solutions without ground truth. We describe how training a model implicitly for bias field correction allows using non-medical data for training, achieving a highly generalized model. The implicit approach was compared to a more traditional training based on medical data. Both models were compared to an optimized N4ITK method, with evaluations on six datasets. The implicitly trained model improved the homogeneity of all encountered medical data, and it generalized better for a range of anatomies, than the model trained traditionally. The model achieves a significant speed-up over an optimized N4ITK method—by a factor of $100$, and after training, it also requires no parameters to tune. For tasks such as bias field correction—where ground truth is generally not available, but the characteristics of the corruption are known—implicit training promises to be a fruitful alternative for highly generalized solutions.} }
Endnote
%0 Conference Paper %T MRI bias field correction with an implicitly trained CNN %A Attila Simkó %A Tommy Löfstedt %A Anders Garpebring %A Tufve Nyholm %A Joakim Jonsson %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-simko22a %I PMLR %P 1125--1138 %U https://proceedings.mlr.press/v172/simko22a.html %V 172 %X In magnetic resonance imaging (MRI), bias fields are difficult to correct since they are inherently unknown. They cause intra-volume intensity inhomogeneities which limit the performance of subsequent automatic medical imaging tasks, \eg, tissue-based segmentation. Since the ground truth is unavailable, training a supervised machine learning solution requires approximating the bias fields, which limits the resulting method. We introduce implicit training which sidesteps the inherent lack of data and allows the training of machine learning solutions without ground truth. We describe how training a model implicitly for bias field correction allows using non-medical data for training, achieving a highly generalized model. The implicit approach was compared to a more traditional training based on medical data. Both models were compared to an optimized N4ITK method, with evaluations on six datasets. The implicitly trained model improved the homogeneity of all encountered medical data, and it generalized better for a range of anatomies, than the model trained traditionally. The model achieves a significant speed-up over an optimized N4ITK method—by a factor of $100$, and after training, it also requires no parameters to tune. For tasks such as bias field correction—where ground truth is generally not available, but the characteristics of the corruption are known—implicit training promises to be a fruitful alternative for highly generalized solutions.
APA
Simkó, A., Löfstedt, T., Garpebring, A., Nyholm, T. & Jonsson, J.. (2022). MRI bias field correction with an implicitly trained CNN. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:1125-1138 Available from https://proceedings.mlr.press/v172/simko22a.html.

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