Prostatic cancer computer-assisted detection method and system based on multi-parameter MRI

Abstract

The invention discloses a prostatic cancer computer-assisted detection method and system based on multi-parameter MRI and relates to the field of medical image processing. The method includes the following steps that at a training stage, firstly, a clinical case sample is preprocessed, then a prostate region and a focus candidate region are automatically extracted, and then features of the focus candidate region are calculated and used for training a classifier; at a testing stage, the trained classier is used for classifying the features of the focus candidate region automatically extracted from the tested clinical case sample, and a corresponding diagnosis result is obtained and serves as reference comments to be provided for doctors. A series of quantitative indexes and corresponding malignancy probability values are provided for radiologists, and the doctors can be effectively assisted in diagnosing the prostatic cancer through an MRI image.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a method and system for computer-aided detection of prostate cancer based on multi-parameter MRI. Background technique [0002] PCa (Prostate Cancer, prostate cancer) is the second most common cancer in men in the world. In the United States, about 1 / 6 men will get prostate cancer, and 1 / 36 men will die from this disease. At present, the diagnostic methods for prostate cancer include: TRUS (Trans-rectal Ultrasound, ultrasound-guided transrectal) prostate biopsy and PSA (Prostate-specific Antigen, prostate-specific antigen) serum examination. A prostate biopsy can be very uncomfortable for the patient and serious infections can occur. Prostate-specific antigen is specific to prostate tissue, but not to prostate cancer. There is considerable overlap in serum PSA results between prostate cancer and benign prostatic hyperplasia. [0003] Different from the above two methods,...

AI Technical Summary

Problems solved by technology

[0006] At present, there are some methods to extract pixel features. These methods generally use the statistical features in the local window centered on a certain pixel as the feature of the point. The disadvantage of these methods is that the number of pixels is large, which will cause the amount of training data. Too large, and the pixel features used cannot describe the whole picture of the lesion area, which is not easy to be understood by doctors like regional features

Other methods extract features of lesion candidate areas, such as size, shape, texture, and asymmetry. The disadvantage is that the segmentation of the prostate area itself is a difficult problem, and cancer lesions in the prostate area also require experienced experts. Doctors manually segment to obtain, and at the same time, more effective features are needed to describe the lesion area of ​​prostate cancer

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