Deep residual network-based semantic mammary gland molybdenum target image lump segmentation method

Abstract

The invention discloses a deep residual network-based semantic mammary gland molybdenum target image lump segmentation method. The method comprises the following steps of: labelling pixel categories of lumps and normal tissues corresponding to a collected mammary gland molybdenum target image so as to generate label images, and dividing the mammary gland molybdenum target image and the corresponding label images into training samples and test samples; preprocessing the training samples to form a training data set; constructing a deep residual network, and training the network by utilizing thetraining data set, so as to obtain a deep residual network training model; after a to-be-segmented mammary gland molybdenum target image lump is preprocessed, carrying out binary classification and post-processing on a pixel of the to-be-segmented mammary gland molybdenum target image by utilizing the deep residual network training model, and outputting lump segmentation image to realize semanticsegmentation of the mammary gland molybdenum target image lump. The method is capable of effectively improving the automatic and intelligent levels of mammary gland molybdenum target image lump segmentation, and can be applied to the technical field of assisting radiologists to carry out medical diagnosis.

Description

technical field [0001] The invention relates to the fields of machine learning and digital medical image processing and analysis, in particular to a method for semantic segmentation of tumors in mammography images based on a deep residual network. Background technique [0002] Breast cancer has become a common malignancy among women worldwide and is the leading cause of cancer death in women. The incidence of female breast cancer in my country is getting younger and rising year by year, and as many as 200,000 people die from breast cancer every year, which has brought catastrophic panic to women's health. Early detection to improve breast cancer outcomes and survival remains the cornerstone of breast cancer control. Mammography has high spatial resolution and can display the early symptoms of breast cancer. It is recognized as the most reliable and convenient method for early diagnosis of breast cancer. With the rapid development of computer and image processing technology...

AI Technical Summary

Problems solved by technology

However, the CV model has its own inevitable defects. When the distribution in the foreground and background regions is uneven, the internal and external characteristic parameters of the level set in the CV model cannot accurately express the local characteristics of the image.

On the other hand, the normal tissue near the tumor in the mammography image is very similar to the tumor, and the intensity distribution of these areas is also very uneven

Therefore, when CV is dealing with ROI (region of interest, region of interest) images with low contrast and large fluctuations in gray levels inside and outside the mass, the segmentation results are often not ideal.

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