# Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

arXiv: https://arxiv.org/abs/1702.05174 · HTML: https://mlsanity.com/paper/1702.05174/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2017.

## Built on by (most cited first, 6 of 6)

- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [U-Noise: Learnable Noise Masks for Interpretable Image Segmentation](https://mlsanity.com/paper/2101.05791/) (2021)
- [ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations](https://mlsanity.com/paper/2101.00567/) (2021)
- [Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging](https://mlsanity.com/paper/2209.05771/) (2022)
- [Performance Analysis of UNet and Variants for Medical Image Segmentation](https://mlsanity.com/paper/2309.13013/) (2023)
- [Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations](https://mlsanity.com/paper/2411.00922/) (2024)

## Abstract (arXiv)

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.