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A method for intelligent assisted identification for diseases like pancreatic cancer and pancreatitis

A technology for pancreatitis and pancreatic cancer, applied in the field of intelligent auxiliary identification of pancreatic cancer and pancreatic inflammatory diseases, can solve the problems of time-consuming and labor-intensive, ignoring the improvement of other modal image performance, poor feature representation and generalization ability, etc. , to avoid uncertainty

Active Publication Date: 2018-09-14
SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] From the analysis of the above research, it can be found that the current intelligent auxiliary identification system for pancreatic diseases mostly has the following deficiencies: (1) It is necessary to finely segment the pancreas or lesion area, which requires doctors to have a deep professional background and rich clinical experience, and consumes Time-consuming and labor-intensive, it is inevitable that there will be segmentation errors; (2) Using manual design for feature extraction, the extracted feature representation and generalization ability are poor, and researchers need to conduct in-depth research on the problem area to be solved in order to design Features with better adaptability; (3) The above studies are all conducted on images of a single modality, ignoring the performance improvement that other modality images may bring

Method used

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  • A method for intelligent assisted identification for diseases like pancreatic cancer and pancreatitis
  • A method for intelligent assisted identification for diseases like pancreatic cancer and pancreatitis
  • A method for intelligent assisted identification for diseases like pancreatic cancer and pancreatitis

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Embodiment 1

[0061] The invention discloses an intelligent auxiliary identification method for pancreatic cancer and pancreatic inflammatory diseases. The specific steps are as follows:

[0062] 1) Reading of multi-modal images, and performing gray scale normalization operations;

[0063] 2) Image preprocessing, denoising, registration and other operations are performed on the normalized image obtained in step 1), to obtain a multimodal image with a uniform sampling interval of improved quality, and then perform image fusion;

[0064] 3) According to the multi-modal image and fusion image obtained in step 2), an experienced radiologist draws a rectangle to include the region of interest in the single-mode or fusion image with clear pancreatic structure, that is, select the rectangular target area, And map this region of interest to other modal images, and save the region of interest as a natural image format such as .png, .bmp, etc. that can be recognized by the subsequent classification n...

Embodiment 2

[0106] figure 2 The image in the middle is provided by Shanghai Changhai Hospital, taking PET / CT as an example to illustrate the image fusion process. First, the registered PET image a) and CT image b) are fused into a pseudo-image c), and then transformed into a grayscale image d) through grayscale transformation, so as to fuse the information of the two modalities together , to perform follow-up processing, or directly perform subsequent processing on the pseudo-image. The construction method of the above-mentioned pseudo-image is to use CT images of different HU value ranges as the two channels of the pseudo-image, and the PET image as the third channel.

Embodiment 3

[0108] An example of building a deep pyramid pooled convolutional neural network. Although the specific network structure of the deep pyramid pooled convolutional neural network for each modality image is different, they all include the following four parts:

[0109] 1) The input of any size image, the input image can be processed by decentralization, standardization, ZCA whitening, etc., making it easier to converge during training and speed up the training process;

[0110] 2) Construction of deep convolutional neural network (DCNN), which includes convolutional layer, pooling layer, BN layer, shortconnect, etc., by adjusting network depth, width, optimization algorithm, activation function, learning rate, etc. Excellent, so that it has the best feature extraction ability;

[0111] 3) Pyramid pooling layer, by introducing a pyramid pooling layer, the feature maps of different sizes generated by the convolutional neural network in 2) can be unified into a fully connected laye...

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Abstract

The invention provides a method for intelligent assisted identification for diseases like pancreatic cancer and pancreatitis. The method comprises the steps of performing reading and normalization operation on medical image data of pancreas to obtain normalization images; performing denoising, registration and image fusion on the normalization images to obtain multi-mode fusion images; selecting areas of interest from images in which pancreas structures are displayed clearly and mapping the areas of interest to the other images and at the same time storing the areas of interest in a natural image format which can be recognized by a following classification network; according to the selected areas of interest, extracting, classifying and fusing features of the multi-mode fusion images and building basic classification network models for the fused features; distinguishing classification results of the basic classification networks to obtain a final classification distinguishing result. The method has great universality; the method is suitable for both clinical practice and scientific research in the field of pancreatic cancer and pancreatitis.

Description

technical field [0001] The invention relates to the technical field of intelligent auxiliary diagnosis, in particular to an intelligent auxiliary identification method for pancreatic cancer and pancreatic inflammatory diseases. Background technique [0002] Pancreatic cancer (PC) is a common malignant tumor of the digestive system. Among malignant tumors in my country, its incidence rate ranks 7th, its mortality rate ranks 6th, and the 3-year survival rate is less than 5%. The early symptoms of pancreatic cancer are often not obvious, and when abdominal pain, jaundice, and weight loss appear, it is often in the advanced stage. In the diagnosis of pancreatic cancer, because its clinical manifestations are very similar to other pancreatic inflammatory diseases, such as chronic pancreatitis (CP), both have abdominal pain, indigestion, anorexia, nausea, vomiting, weight loss and obstructive jaundice, etc. In addition, it overlaps with other pancreatic inflammatory diseases in c...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/50G06N3/04G06N3/08G06K9/62G16H30/20G16H50/20
CPCG06N3/08G06T5/50G06T7/0012G16H30/20G16H50/20G06T2207/30096G06T2207/20221G06T2207/20084G06T2207/20081G06N3/045G06F18/2148
Inventor 杨晓冬程超左长京张玉全刘兆邦孙高峰潘桂霞
Owner SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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