Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention

@article{Chung2021LiverSI,
  title={Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention},
  author={Minyoung Chung and Jingyu Lee and Jeongjin Lee and Yeong-Gil Shin},
  journal={Artificial intelligence in medicine},
  year={2021},
  volume={113},
  pages={
          102023
        }
}
OBJECTIVE Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that focus on the performance of generalization and accuracy. METHODS To improve the… Expand

Figures and Tables from this paper

Batch Normalized Convolution Neural Network for Liver Segmentation
TLDR
A Batch Normalization After All Convolutional Neural Network (BATA-Convnet) model is proposed to segment the liver CT images using Deep Learning Technique to maximize the result efficiency. Expand
Bata-Unet: Deep Learning Model for Liver Segmentation
TLDR
BATA-Unet model for liver segmentation, it's based on Unet architecture as backbone but differ in the authors added a batch-normalization layer an after each convolution layer in both construction path and expanding path, which was able to achieve highest dice similarity coefficient. Expand
Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions
TLDR
This paper proposes a shape-aware contour attention module, that exploits distance regression, which can guide the model to focus on the edges between substructures so that it can outperform the conventional contour-based attention method. Expand
Efficient knowledge distillation for liver CT segmentation using growing assistant network
  • Pengcheng Xu, Kyungsang Kim, +6 authors Quanzheng Li
  • Physics, Medicine
  • Physics in medicine and biology
  • 2021
TLDR
A DL-based real-time 3-D liver CT segmentation method, where knowledge distillation (KD) method, known as knowledge transfer from teacher to student models, is incorporated to compress the model while preserving the performance, is proposed. Expand
Identifying Periampullary Regions in MRI Images Using Deep Learning
TLDR
Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images and helps clinicians to identify and locate thePA region using preoperative MRI scanning. Expand
PCAF‐Net: A liver segmentation network based on deep learning
Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
  • Jin Hong, Simon Chun Ho Yu, Weitian Chen
  • Computer Science, Engineering
  • ArXiv
  • 2021
TLDR
A novel unsupervised domain adaptation framework for cross-modality liver segmentation via joint adversarial learning and self-learning is reported and joint semantic-aware and shape-entropy-aware adversarialLearning with postsitu identification manner is proposed to implicitly align the distribution of task-related features extracted from the target domain with those from the source domain. Expand

References

SHOWING 1-10 OF 58 REFERENCES
Deeply self-supervised contour embedded neural network applied to liver segmentation
TLDR
A neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography images, showing that the guided contour features can significantly improve the performance of the segmentation task. Expand
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
TLDR
This work proposes a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end and demonstrates how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies. Expand
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
TLDR
This work proposes an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network, trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. Expand
Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks
TLDR
It is concluded that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures. Expand
DCAN: Deep contour‐aware networks for object instance segmentation from histology images
TLDR
A novel deep contour‐aware network (DCAN) under a unified multi‐task learning framework for more accurate detection and segmentation of objects of interest from histology images is proposed. Expand
Brain tumor segmentation with Deep Neural Networks
TLDR
A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies. Expand
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
TLDR
Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Expand
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
TLDR
An auto‐context version of the VoxResNet is proposed by combining the low‐level image appearance features, implicit shape information, and high‐level context together for further improving the segmentation performance, and achieved the best performance in the 2013 MICCAI MRBrainS challenge. Expand
Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
TLDR
This paper presents a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes, and evaluates the performance of the algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. Expand
Hierarchical, learning-based automatic liver segmentation
TLDR
This paper presents a hierarchical, learning-based approach for automatic and accurate liver segmentation from 3D CT volumes that come from largely diverse sources and are generated by different scanning protocols. Expand
...
1
2
3
4
5
...