# Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study

arXiv: https://arxiv.org/abs/2101.09987 · HTML: https://mlsanity.com/paper/2101.09987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT](https://mlsanity.com/paper/1708.08333/) (2017)
- [Recent progress in semantic image segmentation](https://mlsanity.com/paper/1809.10198/) (2018)

## Abstract (arXiv)

Medical imaging has been employed to support medical diagnosis and treatment. It may also provide crucial information to surgeons to facilitate optimal surgical preplanning and perioperative management. Essentially, semi-automatic organ and tumor segmentation has been studied by many researchers. Recently, with the development of Deep Learning (DL) algorithms, automatic organ segmentation has been gathered lots of attention from the researchers. This paper addresses to propose the most efficient DL architectures for Liver segmentation by adapting and comparing state-of-the-art DL frameworks, studied in different disciplines. These frameworks are implemented and adapted into a Commercial software, 'LiverVision'. It is aimed to reveal the most effective and accurate DL architecture for fully automatic liver segmentation. Equal conditions were provided to all architectures in the experiments so as to measure the effectiveness of algorithms accuracy, and Dice coefficient metrics were also employed to support comparative analysis. Experimental results prove that 'U-Net' and 'SegNet' have been superior in line with the experiments conducted considering the concepts of time, cost, and effectiveness. Considering both architectures, 'SegNet' was observed to be more successful in eliminating false-positive values. Besides, it was seen that the accuracy metric used to measure effectiveness in image segmentation alone was not enough. Results reveal that DL algorithms are able to automate organ segmentation from DICOM images with high accuracy. This contribution is critical for surgical preplanning and motivates author to apply this approach to the different organs and field of medicine.