Method and system for establishing loss function for liver blood vessel segmentation

Abstract

The invention discloses a method and a system for establishing a loss function for liver blood vessel segmentation, and belongs to the technical field of medical image processing and artificial intelligence. The method comprises the following steps: performing morphological corrosion on an original liver blood vessel mask, and removing free miscellaneous points to obtain a second mask; performing morphological expansion on the second mask to obtain a third mask; calculating an intersection of pixel positions, which are equal to 1, in the original mask and the third mask to obtain a fourth mask; calculating a difference set of pixel positions which are equal to 1 in the original mask and the fourth mask to obtain a fifth mask; dividing an original liver blood vessel mask pixel position set into three parts, and establishing a loss function of liver blood vessel segmentation; the system comprises a corrosion module, a removal module, an expansion module, a first calculation module, a second calculation module and an establishment module. The loss function established by the invention can perform different weighting on branch blood vessels, main blood vessels and background pixels, and the optimization direction of the network is more flexibly and accurately controlled.

Description

technical field [0001] The invention relates to the technical fields of medical image processing and artificial intelligence, in particular to a method and system for establishing a loss function for liver vessel segmentation. Background technique [0002] The variation rate of important blood vessels in the human liver is as high as 65%. It is very important to fully understand the anatomical structure and variation of the vascular system in the liver before operation, which is very important for the formulation of liver surgery plan, surgical prognosis and early detection and treatment of complications. Existing 3D reconstruction of liver vessels mainly includes two categories based on deep learning and traditional methods based on threshold and gray gradient. Due to the influence of factors such as CT scanning time and vascular respiration, the contrast agent is unevenly distributed in the blood, which will lead to inconsistent development of the blood vessel area in the ...

AI Technical Summary

Problems solved by technology

[0009] In order to solve the problem of low segmentation accuracy of small branch vessels and vessel tails of liver vessels using the segmentation model of the existing loss function, the present invention provides a method for establishing a loss function for liver vessel segmentation, including the following steps:

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