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Quick full-automatic intracranial aneurysm auxiliary detection post-processing system and method

An intracranial aneurysm and auxiliary detection technology, applied in the fields of medical imaging and biomedicine, can solve the problems of large differences in data performance, need for manual interaction, and prone to missed judgments, etc., and achieve the effect of fast processing speed

Pending Publication Date: 2021-11-02
FUDAN UNIV
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Problems solved by technology

At present, the detection method of intracranial aneurysms mainly relies on the manual identification of professional doctors based on the above medical images. This method is highly subjective, has low repeatability, and is prone to missed judgments.
Other methods such as: 1) The medical image recognition scheme based on deep learning [1], on the one hand, a large amount of high-quality manual labeling data is required to achieve high accuracy; on the other hand, the generalization ability is poor, such as based on MRI training set The model cannot be used for CT images, and the performance of the same model on the data of different medical institutions and different medical equipment manufacturers is quite different.
2) Existing non-deep learning solutions often take a long time and require manual interaction, which is not automatic enough[2]
Due to the relatively time-consuming calculation of eigenvalues, the enhancement of the entire 3D image takes several minutes or even longer

Method used

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  • Quick full-automatic intracranial aneurysm auxiliary detection post-processing system and method
  • Quick full-automatic intracranial aneurysm auxiliary detection post-processing system and method
  • Quick full-automatic intracranial aneurysm auxiliary detection post-processing system and method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0055] Embodiment 1 Image preprocessing

[0056] For MRI and CT images, the main function of this module is to remove the scalp to obtain the regions where the brain and cerebellum are located, and mark other regions as the background (assign their signal values ​​to 0). For the 3D-DSA image, since the blood vessel signal is low signal after subtraction and the background is high signal, the module performs an inversion operation on it to achieve the unity of the input image: the blood vessel is high signal, and other tissues are low signal.

Embodiment 2

[0057] Embodiment 2 Image Adaptive Threshold Classification

[0058] By analyzing the gray histogram of the input image, the thresholds T1 and T2 are obtained, and the image is divided into three categories: background (signal value<T1), low signal blood vessel area (T1≤signal value<T2), high signal blood vessel area (signal value ≥ T2).

Embodiment 3

[0059] Example 3 Automatically find blood vessel seed points

[0060] Perform a connectivity domain analysis on the high-signal blood vessel areas output by module 2, and sort the area volumes from large to small, and get the three largest connected areas M1, M2, and M3, and satisfy the condition: M2≥0.1*M1, M3 ≥0.1*M1. If the M2 or M3 area does not meet the above conditions, it will be excluded. Finally, a pixel point is randomly selected from each area that meets the conditions as a seed point, and the bottom pixel point is generally selected. For MRI and CT images, the program can find up to three seed points, corresponding to the left common carotid artery, right common carotid artery, and vertebral artery. For 3D-DSA images, a seed point can be found, which corresponds to a certain blood vessel under development.

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Abstract

The invention belongs to the technical field of biological medicine and medical imaging, and relates to a quick and full-automatic intracranial aneurysm auxiliary detection post-processing system and method. The system comprises an image preprocessing module, an image adaptive threshold classification module, an automatic blood vessel seed point searching module, a blood vessel segmentation module, a blood vessel center line extraction, segmentation and classification module, an aneurysm enhancement module, an aneurysm screening module and an aneurysm display module. The method can be used for full-automatic detection of intracranial aneurysms of three common medical image data MRI, CT and 3D-DSA, and the processing speed is high.

Description

technical field [0001] The invention belongs to the technical fields of biomedicine and medical imaging, and relates to a fast and automatic intracranial aneurysm auxiliary detection post-processing system and method. Background technique [0002] The prior art discloses that imaging equipment such as magnetic resonance (MRI), computed tomography (CT), and three-dimensional digital subtraction angiography (3D-DSA) can realize visualization of intracranial aneurysms. At present, the detection method of intracranial aneurysm mainly relies on the manual identification of professional physicians based on the above-mentioned medical images. This method is highly subjective, has low repeatability, and is prone to missed judgments. Other methods such as: 1) The medical image recognition scheme based on deep learning [1], on the one hand, a large amount of high-quality manual labeling data is required to achieve high accuracy; on the other hand, the generalization ability is poor, s...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/187
Inventor 蒋李杨鸣方文星
Owner FUDAN UNIV
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