Title: Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation,
Abstract: In this paper, we introduce a simple, yet powerful pipeline for medical image
segmentation that combines Fully Convolutional Networks (FCNs) with Fully
Convolutional Residual Networks (FC-ResNets). We propose and examine a design
that takes particular advantage of recent advances in the understanding of both
Convolutional Neural Networks as well as ResNets. Our approach focuses upon the
importance of a trainable pre-processing when using FC-ResNets and we show that
a low-capacity FCN model can serve as a pre-processor to normalize medical
input data. In our image segmentation pipeline, we use FCNs to obtain
normalized images, which are then iteratively refined by means of a FC-ResNet
to generate a segmentation prediction. As in other fully convolutional
approaches, our pipeline can be used off-the-shelf on different image
modalities. We show that using this pipeline, we exhibit state-of-the-art
performance on the challenging Electron Microscopy benchmark, when compared to
other 2D methods. We improve segmentation results on CT images of liver
lesions, when contrasting with standard FCN methods. Moreover, when applying
our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we
reach results that are competitive even when compared to 3D methods. The
obtained results illustrate the strong potential and versatility of the
pipeline by achieving highly accurate results on multi-modality images from
different anatomical regions and organs.