Neural network prediction method for colorectal cancer treatment effect based on MRI and CT images

Abstract

The invention relates to a neural network prediction method for colorectal cancer treatment effect based on MRI and CT images. The method comprises the following steps: firstly segmenting a lesion through a deep learning network, and then automatically extracting a region of interest (ROI) according to a segmentation result. And finally, MRI and CT features are fused through a channel, and pCR classification is carried out by using a convolutional neural network. The method provided by the invention can reduce the complexity and time consumption of manual segmentation of the tumor region in predicting the pCR of the patient.

Description

technical field [0001] The invention relates to a neural network prediction method for the treatment effect of colorectal cancer based on MRI and CT images. Background technique [0002] The patent with publication number CN111210909A discloses a deep neural network-based automatic diagnosis system for rectal cancer T staging, including a deep neural network model, which includes: a feature extraction network, a region generation network, a pooling layer, a classification and regression layer; first, use the ResNet-50 model to learn and judge the layer type of the entire input image, learn to judge the orientation of the image, and establish a layer recognition module; then, the ResNet101 model is used as the basic network. A target detection model is trained at each image level to delineate the tumor area and determine the T stage of the tumor. The system has consistent processing results and comparable accuracy for easy integration and large-scale applications [0003] T...

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Problems solved by technology

[0004] Disadvantage 1: Patent Publication No. CN111210909A, this method is easily interfered by data set training when delineating the lesion area, which may easily lead to misjudgment of lesion area discrimination

[0005] Disadvantage 2: The patent with publication number CN113345576A only uses CT multimodal images, and cannot make good use of the rich image information contained in CT and MRI images

However, traditional MRI and CT image fusion techniques have the potential for characteristic artifacts due to the peristalsis of most organs

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