Prostate image registration system and method based on automatic segmentation and pelvis alignment

Abstract

The invention discloses a prostate image registration system and method based on automatic segmentation and pelvis alignment, and mainly solves the problems of large modal difference and low registration precision when prostate multi-modal medical image registration is realized in the prior art. The implementation scheme includes: performing joint segmentation on the prostate CT image sequence andthe prostate MRI image sequence by using a trained prostate multi-modal image segmentation network U-net to obtain a prostate CT image segmentation result image set C and a prostate MRI image segmentation result image set M; and performing two-stage registration on the two image sets C and M to obtain a final registration image set L; and finally, fusing and displaying the final registration image set L and the prostate CT image segmentation result image set C. According to the invention, the prostate multi-modal image difference is reduced, the problem of small-deformation image registrationis solved, the multi-modal image registration efficiency and registration precision are improved, and the method can be used for observing the shape and position change of a prostate tumor anatomicalpart.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image registration system and method, which can be used to observe the shape and position changes of anatomical parts of prostate tumors, realize tracking of lesion parts and adjust radiotherapy plans. Background technique [0002] Image registration refers to aligning two images into a common coordinate system so that changes between the two can be monitored. Registration based on feature points is one of the most commonly used image registration methods. Feature points need to be able to be distinguished from adjacent image points. If this condition is not met, it is impossible to uniquely match it with a corresponding point in another image. Therefore, a feature neighborhood should be sufficiently different from the neighborhood obtained after a small shift. The feature points that are easy to identify and extract in the image include intersection points b...

AI Technical Summary

Problems solved by technology

Because this method only performs registration for the prostate organ, ignoring other information of the pelvic image, the registration result is inaccurate, and it is difficult to provide a reliable basis for clinical application

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