TY - GEN
T1 - Explainability-guided Mathematical Model-Based Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy
AU - Peng, Tao
AU - Wu, Yiyun
AU - Zhao, Jing
AU - Zhang, Bo
AU - Wang, Jin
AU - Cai, Jing
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Accurate segmentation of the prostate is important to image-guided prostate biopsy and brachytherapy treatment planning. However, the incompleteness of prostate boundary increases the challenges in the automatic ultrasound prostate segmentation task. In this work, an automatic coarse-to-fine framework for prostate segmentation was developed and tested. Our framework has four metrics: first, it combines the ability of deep learning model to automatically locate the prostate and integrates the characteristics of principal curve that can automatically fit the data center for refinement. Second, to well balance the accuracy and efficiency of our method, we proposed an intelligent determination of the data radius algorithm-based modified polygon tracking method. Third, we modified the traditional quantum evolution network by adding the numerous-operator scheme and global optimum search scheme for ensuring population diversity and achieving the optimal model parameters. Fourth, we found a suitable mathematical function expressed by the parameters of the machine learning model to smooth the contour of the prostate. Results on the multiple datasets demonstrate that our method has good segmentation performance.
AB - Accurate segmentation of the prostate is important to image-guided prostate biopsy and brachytherapy treatment planning. However, the incompleteness of prostate boundary increases the challenges in the automatic ultrasound prostate segmentation task. In this work, an automatic coarse-to-fine framework for prostate segmentation was developed and tested. Our framework has four metrics: first, it combines the ability of deep learning model to automatically locate the prostate and integrates the characteristics of principal curve that can automatically fit the data center for refinement. Second, to well balance the accuracy and efficiency of our method, we proposed an intelligent determination of the data radius algorithm-based modified polygon tracking method. Third, we modified the traditional quantum evolution network by adding the numerous-operator scheme and global optimum search scheme for ensuring population diversity and achieving the optimal model parameters. Fourth, we found a suitable mathematical function expressed by the parameters of the machine learning model to smooth the contour of the prostate. Results on the multiple datasets demonstrate that our method has good segmentation performance.
KW - a suitable mathematical function
KW - improved quantum evolution network
KW - modified polygon tracking method
KW - Ultrasound prostate segmentation
UR - http://www.scopus.com/inward/record.url?scp=85146694958&partnerID=8YFLogxK
U2 - 10.1109/BIBM55620.2022.9995677
DO - 10.1109/BIBM55620.2022.9995677
M3 - Conference article published in proceeding or book
AN - SCOPUS:85146694958
T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
SP - 1126
EP - 1131
BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
A2 - Adjeroh, Donald
A2 - Long, Qi
A2 - Shi, Xinghua
A2 - Guo, Fei
A2 - Hu, Xiaohua
A2 - Aluru, Srinivas
A2 - Narasimhan, Giri
A2 - Wang, Jianxin
A2 - Kang, Mingon
A2 - Mondal, Ananda M.
A2 - Liu, Jin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Y2 - 6 December 2022 through 8 December 2022
ER -