SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction. (December 2022) - Yan, Zhisheng; Su, Yujing; Sun, Haixia; Yu, Haiyang; Ma, Wanteng; Chi, Honghui; Cao, Huihui; Chang, Qing
Highlights: Image segmentation models based on SegNet and improved SegNet deep learning networks to assist in cardiovascular disease diagnosis. Improved the Encoder module of SegNet using the Pyramid Pooling module to improve the recognition accuracy of images. Using the depth-separable convolution module instead of the normal convolution in the Encoder module. The model can improve efficiency and accuracy of cardiac MRI segmentation. Abstract: Objective: A set of cardiac MRI short-axis image dataset is constructed, and an automatic segmentation based on an improved SegNet model is developed to evaluate its performance based on deep learning techniques. Methods: The Affiliated Hospital of Qingdao University collected 1354 cardiac MRI between 2019 and 2022, and the dataset was divided into four categories: for the diagnosis of cardiac hypertrophy and myocardial infraction and normal control group by manual annotation to establish a cardiac MRI library. On the basis, the training set, validation set and test set were separated. SegNet is a classical deep learning segmentation network, which borrows part of the classical convolutional neural network, that pixelates the region of an object in an image division of levels. Its implementation consists of a convolutional neural network. Aiming at the problems of low accuracy and poor generalization ability of current deep learning frameworks in medical image segmentation, this paper proposes a semantic segmentation method based on deep separable convolutional network to improve the SegNet model, and trains the data set. Tensorflow framework was used to train the model and the experiment detection achieves good results. Results: In the validation experiment, the sensitivity and specificity of the improved SegNet model in the segmentation of left ventricular MRI were 0.889, 0.965, Dice coefficient was 0.878, Jaccard coefficient was 0.955, and Hausdorff distance was 10.163 mm, showing good segmentation effect. Conclusion: The segmentation accuracy of the deep learning model developed in this paper can meet the requirements of most clinical medicine applications, and provides technical support for left ventricular identification in cardiac MRI.