Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network

@article{Rafiei2018LiverSI,
  title={Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network},
  author={Shima Rafiei and Ebrahim Nasr-Esfahani and S. Mohamad R. Soroushmehr and Nader Karimi and Shadrokh Samavi and Kayvan Najarian},
  journal={2018 25th IEEE International Conference on Image Processing (ICIP)},
  year={2018},
  pages={2067-2071}
}
The need for CT scan analysis is growing for diagnosis and therapy of abdominal organs. Automatic organ segmentation of abdominal CT scan can help radiologists analyze the scans faster, and diagnose disease and injury more accurately. However, existing methods are not efficient enough to perform the segmentation process for victims of accidents and emergency situations. In this paper, we propose an efficient liver segmentation with our 3D to 2D fully convolution network (3D-2D-FCN). The… 
View on IEEE
arxiv.org
31 Citations
Highly Influential Citations
1
Background Citations
8
Methods Citations
8
Results Citations
1

Figures and Tables from this paper

31 Citations

Segmentation of Liver in CT images using 3D-Res-UNet

The Modified U-Net architecture that is utilized in this project has used the free-source Monai Framework and is illustrated a deep neural network architecture with the ability to segment medical pictures.

Liver Segmentation in CT Images with Adversarial Learning

A cascade model for liver segmentation from CT images is proposed, which uses cascade U-nets with adversarial learning to obtain more accurate segmentation results.

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-scale Computed Tomography Data

  • Bo WangQingsen Yan Zheng You
  • Computer Science, Medicine
    2020 25th International Conference on Pattern Recognition (ICPR)
  • 2021
This work proposes the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning, and evaluates several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences.

V-Net Based Liver and Tumor Segmentation Using 3D ConditionalRandom Fields and Graph Cuts Method

The present study was performed to propose more efficient deep learning based approach for liver and tumor segmentation from 3D CT scans using V-Net and two different post-processing methods: Conditional Random Field (CRF) and Graph Cuts.

A Comprehensive CT Dataset for Liver Computer Assisted Diagnosis

This work constructs the first comprehensive liver dataset, called ComLiver, which consists of multiple liver-related tasks and manually marks the elaborate labels for each task and introduces vessel instance segmentation, couinaud segmentation tasks based on previous tasks, to better exploit the advanced tasks in liver therapy.

Segmentation of Liver Anatomy by Combining 3D U-Net Approaches

This work proposes using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U- net models inside the obtained volumes, and shows that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models.

Tumor Segmentation in Brain MRI using Fully Convolutional Network

A deep Fully Convolutional Network designed using the TensorFlow library, which can successfully perform segmentation tasks in medical images and achieves a low Dice Score coefficient for a training of 100 epochs.

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning

A multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipSE parameters.

Semi-automatic Detection of Liver Lesions in CT-Images using Digital Image Processing Techniques and Otsu ’ s Thresholding Algorithm

This method is effective in detecting of liver lesions but it take time in the process of calculation , so Otsu work on to develop its technique to new one name it 'two dimensional OTSu’s method' and characterized by effective and less calculation time.

Cascade U-ResNets for Simultaneous Liver and Lesion Segmentation

This work proposes a novel deep learning network (cascade U-ResNets) to produce liver and lesion segmentation simultaneously, and investigates the performance of 5 selected loss functions, WCE (Weighted Cross Entropy), DL (Dice Loss), WDL ( Weighted Dice Loss), TL (Teverskry Loss) and WTL (Weighting Teversky Loss), with the authors' cascade U- ResNets.

References

SHOWING 1-10 OF 16 REFERENCES

Liver Segmentation from CT Images Based on Region Growing Method

A liver segmentation method based on region growing approach is proposed, and the superior liver region is extracted by applying the morphologic operation on a variety of CT images.

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

An automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution and adapted into a segmentation model which is globally optimized in a surface evolution way is proposed.

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

A novel 3D deeply supervised network (3D DSN) is presented which takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference and introduces a deep supervision mechanism during the learning process to combat potential optimization difficulties.

Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation

A general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlasRegistration and patch-based segmentation, two widely used methods in brain segmentation.

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors

BRIEFnet: Deep Pancreas Segmentation Using Binary Sparse Convolutions

This work proposes to use binary sparse convolutions in the first layer as a particularly effective approach to reduce complexity while achieving high accuracy in segmentation of pancreas segmentation in CT.

Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes

A new, efficient, probabilistic algorithm for the automatic analysis of 3D medical images, given an input CT volume, which automatically detects and localizes the anatomical structures within, accurately and efficiently.

Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT

There is substantial room for improvement in image registration for abdominal CT, due to the overall low DSC values, and substantial portion of low-performing outliers.

Regression forests for efficient anatomy detection and localization in computed tomography scans

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.