# Automatic 3D liver location and segmentation via convolutional neural networks and graph cut

arXiv: https://arxiv.org/abs/1605.03012 · HTML: https://mlsanity.com/paper/1605.03012/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2016.

## Built on by (most cited first, 15 of 15)

- [Efficient Medical Image Segmentation Based on Knowledge Distillation](https://mlsanity.com/paper/2108.09987/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation](https://mlsanity.com/paper/2205.11096/) (2022)
- [Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation](https://mlsanity.com/paper/2102.12056/) (2021)
- [MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2303.15826/) (2023)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2105.07672/) (2021)
- [Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.06987/) (2022)
- [Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation](https://mlsanity.com/paper/2108.06761/) (2021)
- [LiMT: A Multi-task Liver Image Benchmark Dataset](https://mlsanity.com/paper/2511.19889/) (2025)
- [SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation](https://mlsanity.com/paper/2109.03435/) (2021)
- [Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice](https://mlsanity.com/paper/2110.02750/) (2021)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation](https://mlsanity.com/paper/2211.08146/) (2022)
- [AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI](https://mlsanity.com/paper/2406.07688/) (2024)

## Abstract (arXiv)

Purpose Segmentation of the liver from abdominal computed tomography (CT) image is an essential step in some computer assisted clinical interventions, such as surgery planning for living donor liver transplant (LDLT), radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment liver in CT scans. Methods The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural networks (CNNs); (ii) accuracy refinement of initial segmentation with graph cut and the previously learned probability map. Results The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb. For the MICCAI-Sliver07 test set, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root mean square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9%, 2.7%, 0.91%, 1.88 mm, and 18.94 mm, respectively. In the case of 20 3Dircadb data, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36%, 0.97%, 1.89%, 4.15 mm and 33.14 mm, respectively. Conclusion The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.