A Densely Connected UNet3D Network Combined Attention Mechanism for Liver and 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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AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes

IEEE Access ◽

10.1109/access.2019.2899608 ◽

2019 ◽

Vol 7 ◽

pp. 24898-24909 ◽

Cited By ~ 19

Author(s):

Huiyan Jiang ◽

Tianyu Shi ◽

Zhiqi Bai ◽

Liangliang Huang

Keyword(s):

Liver Tumor ◽

Attention Mechanism ◽

Tumor Segmentation ◽

Liver Tumor Segmentation

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Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation

Frontiers in Oncology ◽

10.3389/fonc.2021.680807 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shunyao Luan ◽

Xudong Xue ◽

Yi Ding ◽

Wei Wei ◽

Benpeng Zhu

Keyword(s):

Neural Network ◽

Liver Tumor ◽

Semantic Information ◽

Liver Tumors ◽

Ct Images ◽

Attention Mechanism ◽

Tumor Segmentation ◽

Expansion Path ◽

Liver Tumor Segmentation ◽

End To End

PurposeAccurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.MethodsFirst, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.ResultsIn this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors’ manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.ConclusionS-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

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3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

BioMed Research International ◽

10.1155/2017/5207685 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 15

Author(s):

Weiwei Wu ◽

Shuicai Wu ◽

Zhuhuang Zhou ◽

Rui Zhang ◽

Yanhua Zhang

Keyword(s):

Liver Tumor ◽

Processing Time ◽

Spatial Information ◽

Liver Tumors ◽

Graph Cuts ◽

Ct Images ◽

Tumor Segmentation ◽

Dice Similarity Coefficient ◽

Seed Point ◽

Liver Tumor Segmentation

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

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

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.605132 ◽

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.

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