## 1 Introduction  
Prostate cancer is the most common cancer among men in the United States in 2019. According to the latest survey by the American Cancer Society [1], there are approximately 174,650 new prostate cancer patients, and 31,620 people have died of prostate cancer In the past few decades, various methods have been used for prostate cancer detection. Transrectal ultrasound (TRUS) can also be referred to as prostate ultrasound or intrarectal ultrasound. It is used to observe the prostate and surrounding tissues. Ultrasonic transducers (also called probes) send sound waves through the rectal wall to the prostate and surrounding tissues. A computer analyzes the waveform (called an echo) reflected from an organ and converts it into an image that a doctor sees on a video screen [2]. In the treatment of prostate cancer, the use of brachytherapy to implant small radioactive particles in the prostate is made in the surgical environment. Before implanting radioactive particles, the volume and shape of the prostate area need to be calculated accurately. Therefore, it is very important to segment the prostate accurately in the TRUS image. At present, due to the real-time nature of TRUS images, low cost, and easy operation [3], it has become one of the important tools for doctors to diagnose prostate cancer clinically. The guided biopsy is the standard technique for obtaining whole body prostate tissue specimens. On the one hand, during the clinical diagnosis process, the probe was used to observe the prostate on all cross-sections and missing surfaces. It was found that the peripheral gland had low echo changes or other unexplained echo changes inside the glands, and the glands were not paired. A series of changes in external features such as bulging or interruption of the capsule and dilation or disappearance of the seminal vesicles will all be considered that the detected prostate target may be cancerous. On the other hand, in the imaging process of TRUS images, the image itself has problems such as low signal-to-noise ratio and high similarity between the artifact area and the target area. This problem has caused a lot of prostate image segmentation algorithms to a certain extent. This is a big challenge. Therefore, the high-precision TRUS image real-time automatic segmentation method has become one of the important requirements for the clinical diagnosis of prostate cancer. The current prostate segmentation is mainly based on contour and shape segmentation, region-based segmentation, and segmentation based on supervised and unsupervised classification methods. [PERSON] proposed the active shape model (ASM) to generate a binary image from the initial pose, then Multilinear Principal Component Analysis (MPCA) was used to adjust the initial pose to a grayscale ultrasound image and finally adjust the ASM to a grayscale ultrasound image to generate the final prostate contour [4]. However, this method has a slow initialization speed due to the long initialization time. Although the accuracy is high, the real-time segmentation of the TRUS image still fails to meet the requirements. Contour and shape-based segmentation method were proposed by [PERSON] and [PERSON] [5]. This method is a collaborative boundary that includes shape priors and a region-based active contour model. In this work, the contour lines that segment the boundaries of objects are represented using the level set method and evolved by minimizing variational energy. The contribution of the new method of this work is to learn the priors of the shape of the target object of interest and integrate it into a hybrid active contours model to segment all overlapping and non-overlapping objects in the image simultaneously. However, this method can only be more accurate if the boundary of the sub-segmentation target is very clear. As shown in Figure 1, the TRUS image has weak borders between adjacent prostate tissue and non-prostate tissue, and features are very similar. As a result, the application of this method to TRUS images is very limited.