Multi- objective organ-at-risk automatic segmentation method, device and system based on deep learning

Abstract

The invention discloses a multi-objective organ-at-risk automatic segmentation method, device and system based on deep learning. The method comprises the steps of receiving an input image of a patient; carrying out format conversion on the input image of the patient, and converting the input image into JPEG format data; inputting the JPEG format data into an Overfeat positioning detection networktrained according to a physician manual segmentation result, and automatically selecting a region of interest containing multi-objective organ-at-risk; inputting the automatically selected region of interest into an FCN initialization segmentation network, and carrying out contour inference; carrying out the coordinate processing of the initial boundary contour obtained through the contour inference and the received artificial mark boundary, mapping the initial boundary contour and the received artificial mark boundary to an input image, extracting a DAISY feature, and obtaining a DAISY feature image; and inputting the DAISY feature image into a deep belief network trained according to a physician manual segmentation result to obtain an accurate segmentation boundary of organ-at-risk, namely a segmentation result.

Description

technical field [0001] The disclosure belongs to the technical field of organ-at-risk segmentation and detection, and relates to a method, device and system for automatic multi-target organ-at-risk segmentation based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Pituitary tumors are a group of tumors that arise from the residual cells of the anterior and posterior pituitary glands and the craniopharynx. Arteries at the base of the brain, etc., leading to serious impairment of corresponding functions. Among them, the optic chiasm, optic nerve, and brainstem are most commonly compressed by pituitary tumors. Pituitary tumors compress the optic chiasm and optic nerves, resulting in poor vision, visual field changes, blindness and other obstacles; pituitary tumors compress the brainstem, seriously affecting the patient's ...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the shortcomings of the prior art that require manual selection of features and poor algorithm robustness, one or more embodiments of the present disclosure provide a method, device and system for automatic multi-target organ-at-risk segmentation based on deep learning, Receive the MR image of the patient, use the advanced deep learning neural network to automatically locate and detect the position of the organ at risk, and then use the automatic segmentation network to learn the manual segmentation results of the physicist for initial segmentation, and then use the precise segmentation to obtain the position and contour information of the organ at risk , can automatically segment the optic chiasm, optic nerve and brainstem at the same time, and send the output results to the physicist's surgery planning system to assist gamma knife radiation therapy for pituitary tumors

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