Abstract
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.
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The authors acknowledge the funding support from the National Institute of Health (R01 EB027898).
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Peng, T., Tang, C., Wang, J. (2022). Prostate Segmentation of Ultrasound Images Based on Interpretable-Guided Mathematical Model. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13141. Springer, Cham. https://doi.org/10.1007/978-3-030-98358-1_14
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