scholarly journals RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

2020 ◽  
Vol 8 ◽  
Author(s):  
Qiangguo Jin ◽  
Zhaopeng Meng ◽  
Changming Sun ◽  
Hui Cui ◽  
Ran Su
Keyword(s):  
Liver Tumor ◽  
Contextual Information ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Volumetric Images ◽  
Liver Tumor Segmentation ◽  
Liver Region ◽  
High Level

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.

Fully Convolutional DenseNet with Attention Mechanism for Liver Lesion Segmentation in MRI Images

2021 ◽  
Vol 11 (8) ◽  
pp. 2231-2242
Author(s):  
Fei Gao ◽  
Kai Qiao ◽  
Jinjin Hai ◽  
Bin Yan ◽  
Minghui Wu ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Feature Fusion ◽  
Liver Tumors ◽  
Contextual Information ◽  
Liver Lesion ◽  
Attention Mechanism ◽  
Tumor Segmentation ◽  
Semantic Features ◽  
Lesion Segmentation ◽  
High Level

The goal of this research is to achieve accurate segmentation of liver tumors in noncontrast T2-weighted magnetic resonance imaging. As liver tumors and adjacent organs are represented by pixels of very similar gray intensity, segmentation is challenging, and the presence of different sizes of liver tumor makes segmentation more difficult. Differing from previous work to capture contextual information using multiscale feature fusion with concatenation, attention mechanism is added to our segmentation model to extract precise global contextual information for pixel labeling without requiring complex dilated convolution. This study describe a liver lesion segmentation model derived from FC-DenseNet with attention mechanism. Specifically, a global attention module (GAM) is added to up-sampling path, and high-level features are processed by the GAM to generating weighting information for guiding high resolution detail features recovery. High-level features are very effective for accurate category classification, but relatively weak at pixel classification and predicting restoration of the original resolution, so the fusion of high-level semantic features and low-level detail features can improve segmentation accuracy. A weighted focal loss function is used to solve the problem of lesion area occupying a relatively low proportion of the whole image, and to deal with the disequilibrium of foreground and background in the training liver lesion images. Experimental results show our segmentation model can automatically segment liver tumors from complete MRI images, and the addition of the GAM model can effectively improve liver tumor segmentation. Our algorithms have obvious advantages over other CNN algorithms and traditional manual methods of feature extraction.

scholarly journals Rethinking Pooling Operation for Liver and Liver-Tumor Segmentations

2022 ◽  
Vol 1 ◽  
Author(s):  
Junchao Lei ◽  
Tao Lei ◽  
Weiqiang Zhao ◽  
Mingyuan Xue ◽  
Xiaogang Du ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Network Performance ◽  
Feature Fusion ◽  
Feature Learning ◽  
Information Loss ◽  
Medical Image Segmentation ◽  
Image Resolution ◽  
Learning Ability ◽  
Tumor Segmentation ◽  
Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have been widely used in medical image segmentation due to their excellent feature learning ability. In these DCNNs, the pooling operation is usually used for image down-sampling, which can gradually reduce the image resolution and thus expands the receptive field of convolution kernel. Although the pooling operation has the above advantages, it inevitably causes information loss during the down-sampling of the pooling process. This paper proposes an effective weighted pooling operation to address the problem of information loss. First, we set up a pooling window with learnable parameters, and then update these parameters during the training process. Secondly, we use weighted pooling to improve the full-scale skip connection and enhance the multi-scale feature fusion. We evaluated weighted pooling on two public benchmark datasets, the LiTS2017 and the CHAOS. The experimental results show that the proposed weighted pooling operation effectively improve network performance and improve the accuracy of liver and liver-tumor segmentation.

A Densely Connected UNet3D Network Combined Attention Mechanism for Liver and Tumor Segmentation

2021 ◽  
Vol 11 (5) ◽  
pp. 1463-1470
Author(s):  
Hewen Xi ◽  
Junxi Chen ◽  
Dongping Xiong ◽  
Xiaofeng He ◽  
Aiping Qu ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Medical Image ◽  
Spatial Information ◽  
Rapid Development ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Tumor Segmentation ◽  
Shallow Layer ◽  
Liver Tumor Segmentation

In recent years, liver cancer has become one of the five dangerous cancers due to the highest mortality ratios worldwide. Automatic tumor segmentation is a most important task to help radiologists and oncologists to analyze liver CT images. With the rapid development of Convolutional Neural Network (CNN), UNet2D have been widely applied in medical image segmentation. But 2D convolutions cannot extract more important spatial information, making it difficult for the network to learn powerful features between slices. In order to address the problems, we proposed a new densely connected UNet3D network combined attention mechanism (Att-DialResUNet3D) for liver and tumor segmentation. During coding and decoding stages, UNet3D used residual convolution with jagged structure blocks to decrease spatial hierarchical information loss. UNet3D applied an attention mechanism by using the long-range connections between the encoder and decoder to increase the ability of learning important information network. Dense connection decreases the gradient dissipation, and deep supervision can train the shallow layer more fully. We evaluated the proposed approach on the MICCAI 2017 liver tumor segmentation challenge (LiTS) dataset. Our approach preceded other research methods and has gained superior performance for liver tumor segmentation.

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