Heart magnetic resonance image segmentation method and device, terminal equipment and storage medium
A magnetic resonance image and heart technology, applied in the field of medical image processing, can solve the problems of low segmentation accuracy of cardiac magnetic resonance images, and achieve the effect of encouraging repeated use, strengthening dissemination, and improving accuracy
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Embodiment 1
[0067] See figure 1 , which is a schematic flow diagram of a cardiac magnetic resonance image segmentation method provided in an embodiment of the present application, the method may include the following steps:
[0068] Step S101 , acquiring a cardiac magnetic resonance image to be segmented.
[0069] Step S102, performing a first data preprocessing operation on the cardiac magnetic resonance image to be segmented.
[0070] It can be understood that the above-mentioned first data preprocessing operation may include but not limited to a normalization operation.
[0071] Specifically, data normalization is performed on the image so that the image matrix of the image has a mean value of 0 and a variance of 1. Certainly, the above-mentioned first preprocessing operation may also include other conventional preprocessing operations, such as filtering, which is not limited here.
[0072] Step S103, input the pre-processed cardiac magnetic resonance image to be segmented into the ...
Embodiment 2
[0085] The U-DenseNet network model in the above embodiment needs to be trained and tested before image segmentation can be performed. This embodiment will introduce and illustrate the training process.
[0086] See image 3 , which is another schematic flow diagram of a cardiac magnetic resonance image segmentation method provided in the embodiment of the present application, the method may include the following steps:
[0087] Step S301. Obtain a training sample set, and perform a second data preprocessing operation on the training sample set.
[0088] Wherein, data preprocessing operations may include normalization operations and data enhancement operations. The training sample set may specifically be the training sample set in the public data set HVSMR2016.
[0089] Optionally, the above-mentioned specific process of performing the second data preprocessing operation on the training sample set may include: performing a normalization operation on the training sample set,...
Embodiment 3
[0113] See Figure 7 , which is a schematic block diagram of a cardiac magnetic resonance image segmentation device provided in an embodiment of the present application, the device comprising:
[0114] An acquisition module 71, configured to acquire cardiac magnetic resonance images to be segmented;
[0115] A first preprocessing module 72, configured to perform a first data preprocessing operation on the cardiac magnetic resonance image to be segmented;
[0116] A classification module 73, configured to input the pre-processed cardiac magnetic resonance image to be segmented into a pre-trained three-dimensional fully convolutional neural network model to obtain a classification result;
[0117] Wherein, the three-dimensional full convolution neural network model is a U-shaped full convolution network, including a contraction part and an expansion part corresponding to the contraction part, and each convolution in the contraction part and the expansion part A dense block is ...
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