Convolutional neural networks with intermediate loss for 3 d super resolution of ct and mri scans

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This paper proposes a convolutional neural network approach for single-image super-resolution of 3D CT and MRI scans. The method uses two CNNs, with the first increasing resolution on two axes and the second increasing it on the third axis. Unlike other methods, it computes loss with respect to the high-resolution ground truth image after the intermediate upscaling layer, in addition to the final layer. This intermediate loss helps produce outputs closer to the ground truth. It also applies Gaussian smoothing with various standard deviations during training to avoid overfitting. Evaluation on two databases shows the approach achieves superior results compared to interpolation schemes and related works, with human observers preferring its outputs in over 97% of cases for 2x and 4x up

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Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution
of CT and MRI Scans
Abstract
Computed Tomography (CT) scanners that are commonly-used in hospitals and medical centers
nowadays produce low-resolution images, e.g. one voxel in the image corresponds to at most
one-cubic millimeter of tissue. In order to accurately segment tumors and make treatment
plans, radiologists and oncologists need CT scans of higher resolution. The same problem
appears in Magnetic Resonance Imaging (MRI). In this paper, we propose an approach for the
single-image super-resolution of 3D CT or MRI scans. Our method is based on deep
convolutional neural networks (CNNs) composed of 10 convolutional layers and an
intermediate upscaling layer that is placed after the first 6 convolutional layers. Our first CNN,
which increases the resolution on two axes (width and height), is followed by a second CNN,
which increases the resolution on the third axis (depth). Different from other methods, we
compute the loss with respect to the ground-truth high-resolution image right after the
upscaling layer, in addition to computing the loss after the last convolutional layer. The
intermediate loss forces our network to produce a better output, closer to the ground-truth. A
widely-used approach to obtain sharp results is to add Gaussian blur using a fixed standard
deviation. In order to avoid overfitting to a fixed standard deviation, we apply Gaussian
smoothing with various standard deviations, unlike other approaches. We evaluate the
proposed method in the context of 2D and 3D super-resolution of CT and MRI scans from two
databases, comparing it to related works from the literature and baselines based on various
interpolation schemes, using 2× and 4× scaling factors. The empirical study shows that our
approach attains superior results to all other methods. Moreover, our subjective image quality
assessment by human observers reveals that both doctors and regular annotators chose our
method in favor of Lanczos interpolation in 97.55% cases for an upscaling factor of 2× and in
96.69% cases for an upscaling factor of 4×. In order to allow others to reproduce our state-of-

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