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 accurate segmentations on a variety of image modalities and different anatomical regions.

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets

“…It may be that if we had increased the capacity of the MIXED model by adding more convolutional layers to cope with the increased variability in the data, then we could have achieved more accurate results. Another possible approach suggested by Drozdxal et al . is to use a low capacity fully convolutional network as a preprocessor to normalize the MRI data first.…”

“…The input volume to the 3D-MIXED model is the FS volume. et al 32 is to use a low capacity fully convolutional network as a preprocessor to normalize the MRI data first. Alternatively, since we were able to achieve a DSC of 92% on just 14 training images, we could simply use domain adaptation and retrain a U-net using a small number of annotated volumes acquired using the desired sequence.…”

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