Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

Patrick Ferdinand Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger, Sunil Tatavarty, Marc Bickel +8 more
10/7/2016
cs.CV

Official code: IBBM/Cascaded-FCN (301 stars, last commit 2017-12-27).

Abstract

Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs). We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions from the predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a dense 3D CRF that accounts for both spatial coherence and appearance. CFCN models were trained in a 2-fold cross-validation on the abdominal CT dataset 3DIRCAD comprising 15 hepatic tumor volumes. Our results show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We experimentally demonstrate the robustness of the proposed method as a decision support system with a high accuracy and speed for usage in daily clinical routine.

View on arXivView PDF

Code Implementations(1)

IBBM/Cascaded-FCNOfficial100%

Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"

301127Jupyter NotebookJun 13, 20168 years agoNOASSERTION
3d-crfcaffedeep-learningdeep-neural-networksfully-convolutional-networks+4 more

Similar Papers

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

98% match1 repo3012017

Hybrid Cascaded Neural Network for Liver Lesion Segmentation

93% match1 repo102019

Detection-aided liver lesion segmentation using deep learning

89% match1 repo992017

Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases

88% match2023

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging

87% match2025

Research on Tumors Segmentation based on Image Enhancement Method

86% match2024

Cite this paper

@article{christ2016automatic,
  title  = {Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields},
  author = {Patrick Ferdinand Christ and Mohamed Ezzeldin A. Elshaer and Florian Ettlinger and Sunil Tatavarty and Marc Bickel and Patrick Bilic and Markus Rempfler and Marco Armbruster and Felix Hofmann and Melvin D'Anastasi and Wieland H. Sommer and Seyed-Ahmad Ahmadi and Bjoern H. Menze},
  year   = {2016},
  eprint = {1610.02177},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1610.02177}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1610.02177.svg)](https://codeofpaper.com/paper/1610.02177)

Discussion