HT-Net: hierarchical context-attention transformer network for medical ct image segmentation

Author(s):  
Mingjun Ma ◽  
Haiying Xia ◽  
Yumei Tan ◽  
Haisheng Li ◽  
Shuxiang Song
Keyword(s):  
2021 ◽  
Vol 91 ◽  
pp. 107024 ◽  
Author(s):  
Xiwang Xie ◽  
Weidong Zhang ◽  
Huadeng Wang ◽  
Lingqiao Li ◽  
Zhengyun Feng ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.


Author(s):  
H.-F. Lee ◽  
P.-C. Huang ◽  
C. Wietholt ◽  
C.-H. Hsu ◽  
K. M. Lin ◽  
...  

2021 ◽  
pp. 420-430
Author(s):  
Youyi Song ◽  
Lequan Yu ◽  
Baiying Lei ◽  
Kup-Sze Choi ◽  
Jing Qin

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 23421-23429 ◽  
Author(s):  
Chengqin Ye ◽  
Wei Wang ◽  
Shanzhuo Zhang ◽  
Kuanquan Wang

Author(s):  
Hiromasa Suzuki ◽  
Hiroyuki Hishida ◽  
Takashi Michikawa ◽  
Yutaka Ohtake ◽  
Satoshi Oota ◽  
...  

2019 ◽  
Vol 46 (11) ◽  
pp. 4970-4982 ◽  
Author(s):  
Azael M. Sousa ◽  
Samuel B. Martins ◽  
Alexandre X. Falcão ◽  
Fabiano Reis ◽  
Ericson Bagatin ◽  
...  

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