Bias field correction in 3D-MRIs using convolutional autoencoders.

Shashank N. Sridhara; Haleh Akrami; Vaishnavi Krishnamurthy; Anand A. Joshi

doi:10.1117/12.2582042

15 February 2021 Bias field correction in 3D-MRIs using convolutional autoencoders.

Shashank N. Sridhara, Haleh Akrami, Vaishnavi Krishnamurthy, Anand A. Joshi

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962H (2021) https://doi.org/10.1117/12.2582042
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Bias Field correction is a crucial step in MRI preprocessing. The bias field affects the intensity uniformity in MRI images. This effect is mostly due to the in-homogeneity in the magnetic fields or variation in magnetic susceptibility during acquisition. The presence of bias field affects the tissue classification stage, as most of the common methods assume uniform intensities across same tissue. We present a deep learning approach that uses an autoencoding architecture to predict the bias field. The performance of the method is evaluated based on tissue classification accuracy compared to the ground truth result. The proposed method outperforms a traditional histogram based method and results in a more accurate tissue classification.

Conference Presentation

Citation Download Citation

Shashank N. Sridhara, Haleh Akrami, Vaishnavi Krishnamurthy, and Anand A. Joshi "Bias field correction in 3D-MRIs using convolutional autoencoders.", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962H (15 February 2021); https://doi.org/10.1117/12.2582042

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