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Hip joint proximal femur segmentation method based on improved U-Net neural network

A proximal femur, neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problem of unsatisfactory model segmentation accuracy, avoid gradient disappearance, improve accuracy, and improve The effect of accuracy

Pending Publication Date: 2021-11-16
JIANGSU JINMA YANGMING INFORMATION TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, neural network frameworks including 3D U-Net and V-Net have been proposed for the segmentation of the proximal femur of the hip joint, but the segmentation accuracy of the obtained models cannot achieve satisfactory results.

Method used

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  • Hip joint proximal femur segmentation method based on improved U-Net neural network
  • Hip joint proximal femur segmentation method based on improved U-Net neural network
  • Hip joint proximal femur segmentation method based on improved U-Net neural network

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Embodiment 1

[0037] Such as figure 1 Shown, a kind of hip joint femur proximal end segmentation method based on improved U-Net neural network that the present invention proposes, comprises:

[0038] S1. Collect the femoral CT image of the patient, and preprocess the femoral CT image; the preprocessing is to perform median filtering on the femoral CT image, and unify the image size to 256×256, and the number of channels is 3;

[0039] S2. Construct a sample library, mark the femoral region, and obtain a training set and a test set;

[0040] S3. Perform data enhancement on the training set;

[0041] S4, build and improve the U-Net neural network model; improve the U-Net neural network model including encoder, decoder, spatial attention module and hole convolution module;

[0042] S5, based on the training set, the improved U-Net neural network model is trained;

[0043]S6. Based on the test set, test the trained improved U-Net neural network model, output segmentation results, and evaluat...

Embodiment 2

[0047] Such as figure 2 As shown, on the basis of the above embodiment, the encoder in this embodiment includes five levels; the first level includes 64 recursive residual units connected in sequence; the second level includes 128 recursive residual units connected in sequence; The third level includes 256 recursive residual units connected in sequence; the fourth level includes 512 recursive residual units connected in sequence; the fifth level includes 1024 recursive residual units connected in sequence. The decoder includes four levels; the first level includes 64 recursive residual units connected in sequence; the second level includes 128 recursive residual units connected in sequence; the third level includes 256 recursive residual units connected in sequence; The fourth level includes 512 recursive residual units connected in sequence; the fifth level includes 1024 recursive residual units connected in sequence.

Embodiment 3

[0049] Such as image 3 As shown in (a), on the basis of the above-mentioned embodiment, the atrous convolution module in this embodiment is a cascaded atrous convolution module; by inserting the cascaded atrous convolution module at the connection between the encoder and the decoder to expand the convolution Kernel receptive field; the cascaded atrous convolution module consists of atrous convolution layers with atrous rates of 2, 4, and 8, and the output of each layer is summed as the output of the module.

[0050] In a further embodiment, the atrous convolution is: k'=k+(k-1)×(r-1). Among them, k is the size of the standard convolution kernel, r is the hole rate of the hole convolution, and k' is the size of the hole convolution kernel.

[0051] In a further embodiment, k=3, r=2, 4, 8, such as image 3 As shown in (b), a hole convolution kernel with a hole rate of 1, 2, and 3 is used for the ordinary 3×3 convolution kernel. The ratio of network parameters reduced by the h...

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Abstract

The invention relates to the technical field of medical image segmentation, in particular to a hip joint proximal femur segmentation method based on an improved U-Net neural network. The method comprises the following steps of: 1, acquiring a patient femur CT image, and preprocessing; 2, constructing a sample library, and marking a femur region to obtain a training set and a test set; 3, performing data enhancement on the training set; 4, constructing an improved U-Net neural network model; 5, training the improved U-Net neural network model; and 6, testing the trained improved U-Net neural network model, outputting a segmentation result, and evaluating the model to obtain model performance. According to the hip joint proximal femur segmentation method, the accuracy of segmentation of the hip joint proximal femur is improved, the segmentation result can serve as an important reference basis for femoral prosthesis reconstruction, the accuracy, objectivity and reliability of diagnosis are improved, and the method has important significance on development of target skeleton segmentation in medical images.

Description

technical field [0001] The invention relates to the technical field of medical image segmentation, in particular to a method for segmenting the proximal femur of the hip joint based on an improved U-Net neural network. Background technique [0002] Osteoporosis is an initially asymptomatic disease of the bones that is most prominent at the hip (proximal femur), spine, or wrist. The hip joint is an important joint connecting the pelvis and the thigh of the human body. It bears weight and controls the activities of the lower limbs. This joint is prone to wear and deformation, which affects people's normal life. Artificial joint replacement has made great progress in the treatment of hip joint diseases. Great success. However, the current femoral repair only depends on the subjective judgment of the physician, and the selected prosthesis is difficult to match the patient's own joints, which affects the recovery of the patient to a certain extent. In order to be able to extrac...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08G06T5/00G06T7/11
CPCG06T7/0012G06T7/11G06N3/08G06T2207/10081G06T2207/20032G06T2207/20021G06T2207/20081G06T2207/20084G06T2207/30008G06N3/045G06F18/214G06T5/70
Inventor 吕江白晓宝朱新成
Owner JIANGSU JINMA YANGMING INFORMATION TECH
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