TY - GEN
T1 - Prostate Segmentation of Ultrasound Images Based on Interpretable-Guided Mathematical Model
AU - Peng, Tao
AU - Tang, Caiyin
AU - Wang, Jing
N1 - Funding Information:
Acknowledgement. The authors acknowledge the funding support from the National Institute of Health (R01 EB027898).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Ultrasound prostate segmentation is challenging due to the low contrast of transrectal ultrasound (TRUS) images and the presence of imaging artifacts such as speckle and shadow regions. In this work, we propose an improved principal curve-based & differential evolution-based ultrasound prostate segmentation method (H-SegMod) based on an interpretable-guided mathematical model. Comparing with existing related studies, H-SegMod has three main merits and contributions: (1) The characteristic of the principal curve on automatically approaching the center of the dataset is utilized by our proposed H-SegMod. (2) When acquiring the data sequences, we use the principal curve-based constraint closed polygonal segment model, which uses different initialization, normalization, and vertex filtering methods. (3) We propose a mathematical map model (realized by differential evolution-based neural network) to describe the smooth prostate contour represented by the output of neural network (i.e., optimized vertices) so that it can match the ground truth contour. Compared with the traditional differential evolution method, we add different mutation steps and loop constraint conditions. Both quantitative and qualitative evaluation studies on a clinical prostate dataset show that our method achieves better segmentation than many state-of-the-art methods.
AB - Ultrasound prostate segmentation is challenging due to the low contrast of transrectal ultrasound (TRUS) images and the presence of imaging artifacts such as speckle and shadow regions. In this work, we propose an improved principal curve-based & differential evolution-based ultrasound prostate segmentation method (H-SegMod) based on an interpretable-guided mathematical model. Comparing with existing related studies, H-SegMod has three main merits and contributions: (1) The characteristic of the principal curve on automatically approaching the center of the dataset is utilized by our proposed H-SegMod. (2) When acquiring the data sequences, we use the principal curve-based constraint closed polygonal segment model, which uses different initialization, normalization, and vertex filtering methods. (3) We propose a mathematical map model (realized by differential evolution-based neural network) to describe the smooth prostate contour represented by the output of neural network (i.e., optimized vertices) so that it can match the ground truth contour. Compared with the traditional differential evolution method, we add different mutation steps and loop constraint conditions. Both quantitative and qualitative evaluation studies on a clinical prostate dataset show that our method achieves better segmentation than many state-of-the-art methods.
KW - Interpretable-guided mathematical model
KW - Principal curve and neural network
KW - Ultrasound prostate segmentation
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U2 - 10.1007/978-3-030-98358-1_14
DO - 10.1007/978-3-030-98358-1_14
M3 - Conference contribution
AN - SCOPUS:85127042397
SN - 9783030983574
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 177
BT - MultiMedia Modeling - 28th International Conference, MMM 2022, Proceedings
A2 - Þór Jónsson, Björn
A2 - Gurrin, Cathal
A2 - Tran, Minh-Triet
A2 - Dang-Nguyen, Duc-Tien
A2 - Hu, Anita Min-Chun
A2 - Huynh Thi Thanh, Binh
A2 - Huet, Benoit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on MultiMedia Modeling, MMM 2022
Y2 - 6 June 2022 through 10 June 2022
ER -