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Computer-aided diagnosis and treatment system based on benign and malignant ovarian tumor prediction model

A computer-aided and predictive model technology, applied in the field of medical image analysis, can solve problems such as the difficulty of special-shaped masses and the difficulty of eliminating noise interference, and achieve the effect of high accuracy and good identification ability

Active Publication Date: 2022-06-28
SICHUAN ACADEMY OF MEDICAL SCI SICHUAN PROVINCIAL PEOPLES HOSPITAL
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] Most of the existing computer-aided diagnosis and treatment programs for ovarian tumors are based on image data or tumor marker feature information for image segmentation and judgment of benign and malignant tumors. These traditional methods have some inherent defects. Metabolites are detected, and the specific situation of tumor metabolism varies from person to person and from disease to disease. These feature information are very effective in detecting whether a tumor exists, but when judging benign or malignant tumors, the statistical feature information However, it cannot cover all situations well, and there are special data caused by factors such as the patient's physique; and it is difficult to eliminate noise interference when judging only by images, and it is difficult to meet small-sized masses and special-shaped masses. correct judgment

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  • Computer-aided diagnosis and treatment system based on benign and malignant ovarian tumor prediction model
  • Computer-aided diagnosis and treatment system based on benign and malignant ovarian tumor prediction model

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Embodiment 1

[0044] A computer-aided diagnosis and treatment system based on a benign and malignant prediction model of ovarian tumors, the system performs the following steps, and the flow chart is as follows: figure 1 shown:

[0045] S1, obtaining ovarian images and corresponding tumor markers;

[0046] S2, inputting the ovarian image and the corresponding tumor markers into the trained ovarian tumor benign and malignant prediction model, and outputting a tumor judgment result, where the judgment result is a benign tumor or a malignant tumor;

[0047] Wherein, the ovarian tumor benign and malignant prediction model includes an image-based tumor segmentation model, an image-based tumor classification model and a lesion classification fusion prediction model;

[0048] The ovarian image is input into the image-based tumor segmentation model to obtain tumor segmentation results; the tumor segmentation results are input into the image-based tumor classification model to obtain image-based tu...

Embodiment 2

[0068] As a specific example, figure 2 The overall framework for the identification of benign and malignant ovarian tumors is given in , and the acquired ovarian images are sequentially processed by the image-based tumor segmentation model and the image-based tumor classification model to obtain the image-based tumor prediction results; then the image-based tumor prediction results are obtained. After feature combination with tumor markers, combined features are generated, and the combined features are input into the lesion classification fusion prediction model to output the benign and malignant prediction results of ovarian tumors.

[0069] The benign and malignant ovarian tumor prediction model is an important part of the system of the present invention, and the training process of the model mainly includes the training of the image-based tumor segmentation model and the training of the lesion classification model. The training process of the model is as follows Figure 5...

Embodiment 3

[0099] The method of the present invention is verified by using the data of 486 patients in a hospital, and the diagnosis results of the 486 patients with ovarian tumor diseases include benign tumors and malignant tumors. The data included the patient's 5 ovarian tumor-related tumor marker test results and menopause status. Among them, tumor markers include alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), cancer antigen (CA125), human epididymal protein 4 (HE4) and cancer antigen 19-9 (CA19-9). The lesions of all cases were diagnosed by postoperative pathological examination, so this dataset is real and reliable. According to the reference range of tumor markers in Table 2, the examination results are processed into binary features. Processing into binary features means that according to the normal range value provided by the doctor, the value in the normal range is converted to 0, which means "normal" , values ​​outside the normal range are converted to 1, meaning "u...

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Abstract

The invention belongs to the technical field of medical image analysis, and particularly relates to a computer-aided diagnosis and treatment system based on an ovarian tumor benign and malignant prediction model. The system executes the following steps: S1, acquiring an ovary image and a corresponding tumor marker; s2, inputting the ovarian image and the corresponding tumor marker into a trained benign and malignant ovarian tumor prediction model, and outputting a tumor judgment result which is a benign tumor or a malignant tumor; wherein the ovarian tumor benign and malignant prediction model comprises an image-based tumor segmentation model, an image-based tumor classification model and a focus classification fusion prediction model. The prediction model obtained by the system is high in accuracy, and has better identification capability for special cases.

Description

technical field [0001] The invention belongs to the technical field of medical image analysis, in particular to a computer-aided diagnosis and treatment system based on a benign and malignant prediction model of ovarian tumors. Background technique [0002] Ovarian cancer is a very common gynecological malignancy, and its lethality ranks 10th among female malignant tumors and 2nd among reproductive system malignant tumors, second only to cervical cancer. The ovaries are located in the deep pelvic cavity, with a special physiological position, and the onset is insidious. There are no specific symptoms in the early stage when a pelvic mass is formed. In addition, the lack of reliable screening methods for ovarian cancer makes early diagnosis difficult. When about 70% of ovarian cancer patients have obvious clinical symptoms, the tumor has entered the middle or advanced stage or has metastasized to distant organs. Moreover, the prognosis of ovarian cancer patients is poor, wi...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06V10/26G06V10/764G06V10/82G06K9/62G06N3/04A61B6/00A61B6/03
CPCG06T7/0012A61B6/032A61B6/5211G06T2207/30096G06N3/048G06N3/045G06F18/241
Inventor 周飞谢尧黄强廖蔚扈拯宁廖宗慧刘晨阳李双庆刘梦娟
Owner SICHUAN ACADEMY OF MEDICAL SCI SICHUAN PROVINCIAL PEOPLES HOSPITAL
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