Femur neck fracture detection method and system based on weak supervision segmentation
A femoral neck fracture and weakly supervised technology, which is applied in the fields of radiological diagnostic instruments, medical science, radiological diagnostic image/data processing, etc., can solve the problems of femoral neck area detection, low efficiency, and poor detection accuracy
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Embodiment 1
[0075] The method for femoral neck fracture detection based on weakly supervised segmentation includes the following detection steps,
[0076] Obtain the detection image, input the obtained femoral neck X-ray film into the constructed and trained detection neural network to detect the femoral part, and obtain the probability segmentation map P probability and femoral neck area detection image P detec ;
[0077] Obtain the fracture type of the femoral neck region, and detect the femoral region image P detec Input to the constructed and trained classification network to obtain the type of femoral fracture;
[0078] Obtain the segmented fracture site, and detect the femoral region image P detec Input the constructed and trained weakly supervised segmentation network to obtain the segmented fracture image P seg ;
[0079] Image fusion is used to obtain the image of the femoral neck fracture area, and the segmented fracture site image P seg with femoral region detection image...
Embodiment 2
[0124]On the basis of Embodiment 1, the system of femoral neck fracture detection based on weak supervision segmentation in this embodiment includes a detection module, a classification module, a weak supervision segmentation module and an image fusion module, and is characterized in that it also includes a femoral fracture detection system based on weak supervision segmentation. Methods of neck fracture detection,
[0125] The detection module inputs the obtained X-rays of the femoral neck into the constructed and trained detection neural network to detect the femoral part, and obtains the probability segmentation map P probability and femoral neck area detection image P detec ;;
[0126] Classification module, the femoral region detection image P detec Input to the constructed and trained classification network to obtain the type of femoral fracture;
[0127] The weakly supervised segmentation module obtains the segmented fracture site and detects the femoral region image...
Embodiment 3
[0130] On the basis of embodiment 1, the steps of constructing neural network and segmentation probability map in this embodiment include,
[0131] Step 1.1, input a set of femoral neck X-ray images P orig , set the initial convolution output channel 64;
[0132] Step 1.2, for the input image P orig Do 2 convolutions of 3×3, and the number of output channels for each convolution is n 2 , batch normalization and ReLU operations are performed after each convolution, and a 2×2 maximum pooling operation is performed after the two convolutions are completed to extract the feature EIG_S2;
[0133] Step 1.3, use the feature EIG_S2 as the input feature, perform two 3×3 convolutions, and the number of output channels for each convolution is n 3 , n 3 =n 2 *2; when n 3 =512, the output feature is EIG_S3, and step 1.4 is performed, otherwise, the output feature EIG_S3 is used as the input feature of step 1.2, and step 1.2 is repeated;
[0134] Step 1.4, perform two 3×3 convolution...
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