Construction method of brain corpus callosum segmentation prediction image for corpus callosum state evaluation

Abstract

The invention discloses a construction method of a brain corpus callosum segmentation prediction image for corpus callosum state evaluation, which comprises the following steps: drawing a corpus callosum initial contour line from an acquired fetal brain ultrasonic image, and calculating a key point offset of the corpus callosum initial contour line; and cutting a brain corpus callosum segmentation prediction image from the fetal brain ultrasound image according to the initial contour line of the corpus callosum and the key point offset. According to the deep neural network model for fetal ultrasound image state analysis, the blank of brain ultrasound image brain corpus callosum state analysis is filled, and a method for evaluating the brain corpus callosum state through a brain ultrasound image is created for the first time.

Description

technical field [0001] The invention relates to the field of medical image segmentation and deep learning, and specifically relates to a method for constructing a predictive image of brain corpus callosum segmentation for corpus callosum state assessment. Background technique [0002] The corpus callosum is located at the floor of the interhemispheric fissure and is the largest commissural fiber in the cerebral hemisphere. Dysplasia of the corpus callosum (Agenesis of Corpus Callosum, ACC) is a congenital abnormality in fetal central nervous system malformation, which refers to the partial or complete loss of the corpus callosum during development. It is important to use imaging examinations to diagnose the development of the corpus callosum during fetal life. effect. [0003] At present, the measurement of the size and position of the corpus callosum still relies on medical imaging technicians to provide it according to the fetal brain ultrasound image annotation, which re...

AI Technical Summary

Problems solved by technology

[0003] At present, the measurement of the size and position of the corpus callosum still relies on medical imaging technicians to provide it according to the fetal brain ultrasound image annotation, which requires high requirements for the operator's experience and technology, and the ultrasound image cannot accurately calculate the volume of the corpus callosum. It also cannot provide an effective basis for the judgment of the sonographer

Therefore, the abnormal detection rate of fetal corpus callosum is low and the error rate is high

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