MRI hip joint inflammation segmentation and classification automatic quantitative grading sequential method

Abstract

The invention discloses an MRI hip joint inflammation segmentation and classification automatic quantitative grading sequential method, solves the problem that hip joint inflammation areas are difficult to quantify and grade due to uneven distribution of shapes, sizes and voxel values of hip joints, and realizes automatic accurate quantification and grading evaluation of the hip joint inflammationareas. According to the method, the hip joint inflammation area label value is modified in an inverse exponential proportion according to the voxel value, so that the problem of poor segmentation result caused by large voxel value difference of the hip joint inflammation area is solved; according to the method, a multi-scale convolution module GMS is designed to improve the segmentation effect ofhip joint inflammation regions with large shape and size differences; aiming at the problem that hip joint inflammation regions with small proportions are difficult to classify, a segmentation modeland a classification model are fused to improve the classification precision. According to the method, integrated automatic quantitative grading of hip joint inflammatory regions with multiple scalesand large voxel value differences is realized, and the quantitative grading efficiency is improved. The method can be used for quantitative and graded automatic treatment of the MRI hip joint inflammation area.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to the segmentation and classification of nuclear magnetic resonance MRI hip joint inflammation lesions, specifically an MRI hip joint inflammation segmentation and classification automatic quantitative grading sequential method, which can be used in MRI images of hip joints in spinal arthritis Automated quantification and grading of inflammation. Background technique [0002] Spondyloarthritis is a chronic, inflammatory and disabling disease. Hip joint damage is the main cause of spondyloarthritis disability. The total annual incidence of total hip replacement is 0.17% to 5%. Therefore, accurate Early detection of hip joint damage in spondyloarthritis helps to assess the overall condition and formulate a diagnosis and treatment plan, thereby reducing the disability rate. Imaging methods have become the main method for early diagnosis and evaluation of spondyloarthrit...

AI Technical Summary

Problems solved by technology

In "Semi-automatic segmentation of JIA-induced inflammation in MRI images of ankle joints" published in "Progress in Biomedical Optics and Imaging" in 2019, a threshold-based inflammation segmentation method was proposed, using the prior information of MRI inflammation to set Thresholds are used to segment inflammation. This method has high requirements for the consistency of voxel values ​​in the inflammatory area. Due to the uneven distribution of voxels in the inflammatory area in MRI data, using this method will result in poor segmentation of the inflammatory area and affect the quantitative accuracy.

The method of lesion analysis based on the deep learning model UNet has been widely used in recent years. However, due to the characteristics of the scale, shape, intensity value, and random distribution of the hip joint inflammation area in MRI of spinal arthritis, the original UNet method cannot fully obtain the MRI hip joint. Accurate location information of the joint inflammation area is prone to missed detection and wrong detection, resulting in poor final quantitative results

At the same time, because the MRI hip joint inflammation area usually accounts for a small proportion in the MRI hip joint image, the common image classification model has poor classification results for the MRI hip joint inflammation area. The accurate segmentation and classification of regions has not yet reached a practical level and needs to be further solved

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