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Accurate segmentation of prostate ultrasound images in the clinic has an important impact on the subsequent diagnosis, and the currently available research findings cannot accurately segment each part. This paper proposed a bimodal shape reconstruction method based on the point distribution model and stream shape learning and segmentation of prostate ultrasound images: target initial localization by random forest indication of the hidden state expression, improvement of the boundary operator to improve the coarse segmentation accuracy, interpolation computation using some gray significant points adjacent to the noisy part of the explicit state expression to recover the shape as a whole. This segmentation method not only reduced the data computation amount, but increased the segmentation reliability. Experiments show that the DSC of the proposed method is 97. 38% and the mIoU is 95.24%, and the accuracy is stronger than the current popular segmentation neural networks. [ABSTRACT FROM AUTHOR]