TY - GEN
T1 - CT images segmentation method of rectal tumor based on modified U-net
AU - Zheng, Biao
AU - Cai, Chenxiao
AU - Ma, Lei
N1 - Funding Information:
*This work is supported by the National Natural Science Foundation of China under Grant No. 61973164 Chenxiao CAI is corresponding author:ccx5281@njust.edu.cn Biao ZHENG and Chenxiao CAI are with School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China Lei MA is with School of information and control engineering, China university of mining and technology, Xuzhou, Jiangsu, 221006, China
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/13
Y1 - 2020/12/13
N2 - Computer-assisted rectal clinical diagnosis is of great significance for the early detection and treatment of rectal cancer. In data processing, it is quite challenging to achieve automatic segmentation due to the blurred boundary between lesions and healthy rectal tissue. To overcome such difficulties, we propose a rectal tumor segmentation method by using a modified U-net, thereby improving the diagnostic efficiency and accuracy. Firstly, the central coordinates of the rectal part to extract the region of interest are determined. Then, the tumor region is determined in the CT image via the YOLOv3 algorithm. Finally, the residual connection and attention mechanism are introduced to reduce the possibility of misjudgment of healthy rectal tissue as lesions to improve the accuracy of the traditional U - net model, and we use the modified U-net model to segment the rectal tumor region. The experiments show the Dice coefficient of this method can reach 83.45 %, which is about 7% higher than the traditional U-net method, and this shows the validation and merits of the proposed algorithm.
AB - Computer-assisted rectal clinical diagnosis is of great significance for the early detection and treatment of rectal cancer. In data processing, it is quite challenging to achieve automatic segmentation due to the blurred boundary between lesions and healthy rectal tissue. To overcome such difficulties, we propose a rectal tumor segmentation method by using a modified U-net, thereby improving the diagnostic efficiency and accuracy. Firstly, the central coordinates of the rectal part to extract the region of interest are determined. Then, the tumor region is determined in the CT image via the YOLOv3 algorithm. Finally, the residual connection and attention mechanism are introduced to reduce the possibility of misjudgment of healthy rectal tissue as lesions to improve the accuracy of the traditional U - net model, and we use the modified U-net model to segment the rectal tumor region. The experiments show the Dice coefficient of this method can reach 83.45 %, which is about 7% higher than the traditional U-net method, and this shows the validation and merits of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85100086493&partnerID=8YFLogxK
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U2 - 10.1109/ICARCV50220.2020.9305381
DO - 10.1109/ICARCV50220.2020.9305381
M3 - Conference contribution
AN - SCOPUS:85100086493
T3 - 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
SP - 672
EP - 677
BT - 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
Y2 - 13 December 2020 through 15 December 2020
ER -
