Magnetic resonance colorectal cancer T stage prediction method and system
A colorectal cancer, prediction method technology, applied in neural learning methods, instruments, neural architectures, etc., can solve the problems of image feature calculation errors, time-consuming, different analysis results, etc., achieve complete feature extraction, reduce workload, improve performance effect
Pending Publication Date: 2022-07-29
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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Problems solved by technology
The basic problems of existing radiomics methods include: First, the image segmentation step usually relies on manual delineation, which is time-consuming and affected by inter-class or intra-class observer differences
Second, even if image segmentation is accurate, there is no standard evaluation method for image feature extraction, and different image features will lead to different analysis results
Since it is difficult to verify the accuracy and repeatability of image features, additional errors may be introduced due to miscalculation of image features
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[0028] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.
[0029] see figure 1 As shown, it is a working flow chart of a preferred embodiment of the magnetic resonance colorectal cancer T stage prediction method of the present invention.
[0030] Step S1, preprocessing the acquired MRI image. in particular:
[0031] The value range of the original MRI image is 0-1024, so the contrast of the original MRI image is general, resulting in a blurred boundary between the high signal in the tumor area and the low signal in the surrounding tissue, and the image in this case has a bad influence on the feature extraction. . After studying the contrast of the tumor area, in this example, the pixels less than 100 are set to 100, and the pixels greater than 800 are set to 800, and then CLAHE histogram equalization is used to suppress noise while enhancing the contrast of MRI images.
[0032] Step S2, ima...
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The invention relates to a magnetic resonance colorectal cancer T stage prediction method. The method comprises the following steps: a, preprocessing an obtained MRI image; b, carrying out image segmentation on the preprocessed image; c, extracting features of the image according to an image segmentation result; d, performing deep learning according to the extracted image features to obtain a convolutional neural network model; and e, predicting the T-stage result of the colorectal cancer by using integrated learning in combination with the image features and the convolutional neural network model. According to the method, the colorectal cancer T stage can be predicted, the prediction performance is improved, and the workload of doctors is reduced.
Description
technical field [0001] The invention relates to a magnetic resonance colorectal cancer T stage prediction method and system. Background technique [0002] Colorectal cancer is one of the most common cancers worldwide, the third most common cancer and the fourth leading cause of death worldwide, with 1-2 million new cases and 700,000 deaths each year. However, with more than half of new cases and deaths attributable to manageable risk factors such as smoking, unhealthy diet, heavy alcohol consumption, physical inactivity and excess weight, colorectal cancer may be preventable. From the perspective of the formation cycle of colorectal cancer, it usually takes 5 to 15 years for benign polyps to develop into advanced tumors. The 5-year survival rate for early-stage colorectal cancer is 90%, while the 5-year survival rate for advanced colorectal cancer is less than 10%. Because colorectal cancer is characterized by insidiousness, long course, early diagnosis, and favorable prog...
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IPC IPC(8): G16H50/30G06V10/26G06V10/40G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG16H50/30G06N3/08G06N3/045G06F18/24323
Inventor 贾富仓马骁
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI