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.

References in corpus (13)

• Deep Residual Learning for Image Recognition
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• Brain Tumor Segmentation with Deep Neural Networks
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Going Deeper with Convolutions
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
• Visualizing and Understanding Convolutional Networks
• Deep Networks with Stochastic Depth
• Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
• DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
• VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
• The Importance of Skip Connections in Biomedical Image Segmentation
• An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation

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