  Purpose: Manual medical image segmentation is an exhausting and
time-consuming task along with high inter-observer variability. In this study,
our objective is to improve the multi-resolution image segmentation performance
of U-Net architecture. Approach: We have proposed a fully convolutional neural
network for image segmentation in a multi-resolution framework. We used U-Net
as the base architecture and modified that to improve its image segmentation
performance. In the proposed architecture (mrU-Net), the input image and its
down-sampled versions were used as the network inputs. We added more
convolution layers to extract features directly from the down-sampled images.
We trained and tested the network on four different medical datasets, including
skin lesion photos, lung computed tomography (CT) images (LUNA dataset), retina
images (DRIVE dataset), and prostate magnetic resonance (MR) images (PROMISE12
dataset). We compared the performance of mrU-Net to U-Net under similar
training and testing conditions. Results: Comparing the results to manual
segmentation labels, mrU-Net achieved average Dice similarity coefficients of
70.6%, 97.9%, 73.6%, and 77.9% for the skin lesion, LUNA, DRIVE, and PROMISE12
segmentation, respectively. For the skin lesion, LUNA, and DRIVE datasets,
mrU-Net outperformed U-Net with significantly higher accuracy and for the
PROMISE12 dataset, both networks achieved similar accuracy. Furthermore, using
mrU-Net led to a faster training rate on LUNA and DRIVE datasets when compared
to U-Net. Conclusions: The striking feature of the proposed architecture is its
higher capability in extracting image-derived features compared to U-Net.
mrU-Net illustrated a faster training rate and slightly more accurate image
segmentation compared to U-Net.

Image and Video Processing;Computer Vision and Pattern Recognition