# Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

arXiv: https://arxiv.org/abs/1707.08037 · HTML: https://mlsanity.com/paper/1707.08037/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

## Built on by (most cited first, 17 of 17)

- [Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation](https://mlsanity.com/paper/2106.06908/) (2021)
- [Deep Implicit Statistical Shape Models for 3D Medical Image Delineation](https://mlsanity.com/paper/2104.02847/) (2021)
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- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
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- [Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration](https://mlsanity.com/paper/2104.07056/) (2021)
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- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Deep Conditional Shape Models for 3D cardiac image segmentation](https://mlsanity.com/paper/2310.10756/) (2023)
- [Deep Compatible Learning for Partially-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2206.09148/) (2022)
- [PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training](https://mlsanity.com/paper/2207.11683/) (2022)
- [A multi-organ point cloud registration algorithm for abdominal CT registration](https://mlsanity.com/paper/2203.08041/) (2022)
- [MLP-GAN for Brain Vessel Image Segmentation](https://mlsanity.com/paper/2207.08265/) (2022)
- [TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors](https://mlsanity.com/paper/2102.02690/) (2021)
- [AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI](https://mlsanity.com/paper/2406.07688/) (2024)

## Abstract (arXiv)

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.