Keywords: 3D U-Net, Image Synthesis, Medical Image Reconstruction, Deep Learning
Abstract: The U-Net is a robust general-purpose deep learning architecture designed for semantic segmentation of medical images, and has been extended to 3D for volumetric applications such as magnetic resonance imaging (MRI) of the human brain. An adaptation of the U-Net to output pixelwise regression values, instead of class labels, based on multichannel input data, has been developed in the remote sensing satellite imaging research domain. The pixelwise regression U-Net has only received limited consideration as a deep learning architecture in medical imaging for the image estimation/synthesis problem, and the limited work so far did not consider the application of 3D multichannel inputs. In this paper, we propose the use of the multichannel input pixelwise regression 3D U-Net (rUNet) for estimation of medical images. Our findings demonstrate that this approach is robust and versatile and can be applied to predicting a pending MRI examination of patients with Alzheimer's disease based on previous rounds of imaging, can perform medical image reconstruction (parametric mapping) in diffusion MRI, and can be applied to the estimation of one type of MRI examination from a collection of other types. Results demonstrate that the rUNet represents a single deep learning architecture capable of solving a variety of image estimation problems. Public domain code is provided.
Paper Type: both
Primary Subject Area: Image Synthesis
Secondary Subject Area: Application: Radiology
Paper Status: original work, not submitted yet
Source Code Url: https://github.com/stfxecutables/Multichannel-input-pixelwise-regression-u-nets
Data Set Url: https://www.humanconnectome.org/, http://adni.loni.usc.edu/, https://www.med.upenn.edu/cbica/brats2020/
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