24-03-2022 дата публикации
Номер: US20220092789A1
Принадлежит:
The present invention discloses an automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network. Under a coarse-to-fine two-step segmentation framework, the method uses a densely connected dilated convolutional neural network as a basis network architecture to obtain multi-scale image feature expression of the target. An initial segmentation probability map of the pancreas is predicted in the coarse segmentation stage. A saliency map is then calculated through saliency transformation based on a geodesic distance transformation. A saliency-aware module is introduced into the feature extraction layer of the densely connected dilated convolutional neural network, and the saliency-aware densely connected dilated convolutional neural network is constructed as the fine segmentation network model. A coarse segmentation model and the fine segmentation model are trained using a training set, respectively. 1. A pancreas CT automatic segmentation method based on a saliency-aware densely connected dilated convolutional neural network , comprising the following steps of:(1) preprocessing of training set, comprising the following steps of:collecting CT volume data and making a standard pancreas segmentation result of the data;{'sub': j', 'j', 'j', 'j, 'denoting 3D CT volume data as X, and slice number of the volume data as L, a corresponding standard segmentation being Y=(y,j=1, . . . , |X|),y={0,1}, where |X| represents a number of all voxels in X, y=1 or y=0 represents that voxel j belongs to the pancreas or a background, respectively;'}Slicing each volume X into two-dimensional image slices alone axial view; and{'sub': 'A,l', 'combining three consecutive images into a three-channel pseudo-color image, denoted as X(l=1, . . . , L)'}{'sub': 'A,l', 'Slicing Y into two-dimensional image slices alone axial view, and combining three consecutive label images into a three-channel label image, denoted as Y(l=1, . . . , L ...
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