H-ProSeg: Hybrid ultrasound prostate segmentation based on explainability-guided mathematical model

Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for image-guided prostate interventions and prostate cancer diagnosis. However, it remains a challenging task for various reasons, including a missing or ambiguous boundary between the prostate and surrounding tissues, the presence of shadow artifacts, intra-prostate intensity heterogeneity, and anatomical variations.Here, we present a hybrid method for prostate segmentation (H-ProSeg) in TRUS images, using a small number of radiologist-defined seed points as the prior points. This method consists of three subnetworks. The first subnetwork uses an improved principal curve-based model to obtain data sequences consisting of seed points and their corresponding projection index. The second subnetwork uses an improved differential evolution-based artificial neural network for training to decrease the model error. The third subnetwork uses the parameters of the artificial neural network to explain the smooth mathematical description of the prostate contour. The performance of the H-ProSeg method was assessed in 55 brachytherapy patients using Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) values.The H-ProSeg method achieved excellent segmentation accuracy, with DSC, Ω, and ACC values of 95.8%, 94.3%, and 95.4%, respectively. Meanwhile, the DSC, Ω, and ACC values of the proposed method were as high as 93.3%, 91.9%, and 93%, respectively, due to the influence of Gaussian noise (standard deviation of Gaussian function, σ = 50). Although the σ increased from 10 to 50, the DSC, Ω, and ACC values fluctuated by a maximum of approximately 2.5%, demonstrating the excellent robustness of our method.Here, we present a hybrid method for accurate and robust prostate ultrasound image segmentation. The H-ProSeg method achieved superior performance compared with current state-of-the-art techniques. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. The proposed models have the potential to improve prostate cancer diagnosis and therapeutic outcomes.