Method and device for constructing an automatic liver segmentation model based on deep learning, computer equipment and storage medium

Abstract

The invention provides a method for constructing an automatic liver segmentation model based on deep learning, and the method comprises the steps: obtaining a three-dimensional image of a sample liver, and obtaining a liver segmentation label of the three-dimensional image of the sample liver; and serving the three-dimensional image of the sample liver and the liver segmentation label as a training set, iterating the liver segmentation model for deep learning training, and obtaining the trained liver segmentation model, wherein a deep learning network adopted by the liver segmentation model is a segmentation network based on combination of UNet / VNet and a channel attention mechanism. The invention further provides a related device, computer equipment and a readable storage medium. Due to the fact that the UNet / VNet and the channel attention mechanism are combined, more image information of the sample liver three-dimensional image is mined through the UNet / VNet, important channel information is endowed with higher weight through the channel attention mechanism, a more accurate prediction result is obtained, and an accurate and efficient liver segmentation result can be provided for doctors.

Description

Technical field [0001] The present invention relates to the field of medical imaging processing, and in particular, to the field of liver segmentation, specifically, a construction method, apparatus, computer device, and storage medium based on deep learning automated liver segmentation model. Background technique [0002] Liver cancer is one of the most common cancer diseases in the world. At present, hepatocellular carcinoma has extruded gastric cancer into the top three of cancer death. The liver is a common site for primary or secondary tumor growth, and their heterogeneous and diffusion shape make it difficult to segment anatomy. Therefore, it is a prerequisite for modern liver surgery to achieve precise inference and measurement of each segment in the liver. [0003] In clinical diagnosis, the liver segment is very time consuming along the Z-axis remote distance to explore the space information, and therefore the automation method is required to be efficiently solved. Recen...

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Problems solved by technology

[0005] However, there are few studies on automatic liver segmentation based on MRI at present, and the traditional algorithm is still used to implement it step by step (Lebre M A, Vacavant A, Grand-Brochier M, et al. Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaudscheme[J].Computers in Biology and Medicine,2019,110(7):42-51), that is, liver segmentation, vessel centerline extraction and reconstruction of liver segmentation

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