Method and device for segmenting liver tumor under CT (Computed Tomography) image

Abstract

The invention discloses a method and a device for segmenting a liver tumor under a CT image. The method comprises the steps: cutting each original CT image of a first preset size in a training set into three adjacent slices of a second preset size, taking a segmentation result of a middle slice as a label, and enabling the corresponding three adjacent slices to form a training sample; inputting one or more training samples into a preset network for training to obtain a trained segmentation model; wherein the preset network comprises a preprocessing module and a full-convolution point cutting network; and for any to-be-detected CT image, inputting three adjacent slices of a first preset size into the trained segmentation model each time, and obtaining a segmentation result of a middle slice based on a joint loss function. According to the scheme, the problems existing in traditional liver tumor image segmentation can be effectively solved, the problems of large imaging difference of liver CT images, different tumor shapes and sizes and the like are solved, and the performance and effect of the automatic liver tumor segmentation method are improved.

Description

technical field [0001] The invention belongs to the technical field of image segmentation, and more specifically relates to a method and device for segmenting liver tumors under CT images. Background technique [0002] The liver is one of the essential organs of the human body and has functions such as metabolism and detoxification. Liver cancer is currently one of the most common cancers in the world, which seriously threatens human life and health. With the development of medical imaging technology, imaging examinations such as computed tomography (abbreviated as CT) and magnetic resonance imaging have been widely used in clinical diagnosis, among which CT technology is the most commonly used means in the examination and diagnosis of liver tumors . At present, the treatment methods for liver tumors mainly include surgical resection, ablation therapy, radiation therapy, etc., but no matter what kind of treatment method is used, it is necessary to accurately understand the ...

AI Technical Summary

Problems solved by technology

[0004] Traditional liver tumor image segmentation requires experienced radiologists to manually identify, label, and segment tumors, which is time-consuming and laborious, and the segmentation results are heavily dependent on the subjective judgments of different physicians

Especially reflected in the preprocessing method of CT scan images: different medical image problems need to set different window intervals, even for the same medical problem, such as liver tumor segmentation, the setting of window intervals varies from person to person, so the traditional The preprocessing method is highly subjective and empirical, and it is difficult to consider the overall data distribution in this way

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