# Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation

arXiv: https://arxiv.org/abs/2103.14301 · HTML: https://mlsanity.com/paper/2103.14301/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields](https://mlsanity.com/paper/1610.02177/) (2016)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)
- [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks](https://mlsanity.com/paper/1702.05970/) (2017)
- [Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation](https://mlsanity.com/paper/1903.11834/) (2019)
- [Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1606.07830/) (2016)
- [Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation](https://mlsanity.com/paper/1710.04540/) (2017)
- [Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation](https://mlsanity.com/paper/1810.03871/) (2018)
- [Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database](https://mlsanity.com/paper/1911.00282/) (2019)

## Built on by (most cited first, 2 of 2)

- [Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation](https://mlsanity.com/paper/2510.08116/) (2025)
- [A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability](https://mlsanity.com/paper/2607.20028/) (2026)

## Abstract (arXiv)

To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomography (CT) and magnetic resonance imaging (MRI) data prior to liver analysis and quantification. This paper investigates the impact of permutation of various preprocessing techniques for CT images, on the automated liver segmentation using deep learning, i.e., U-Net architecture. The study focuses on Hounsfield Unit (HU) windowing, contrast limited adaptive histogram equalization (CLAHE), z-score normalization, median filtering and Block-Matching and 3D (BM3D) filtering. The segmented results show that combination of three techniques; HU-windowing, median filtering and z-score normalization achieve optimal performance with Dice coefficient of 96.93%, 90.77% and 90.84% for training, validation and testing respectively.