To fully exploit Transformer for accurate registration,self-similarity context is used as a feature extractor to extract the semantic information of the voxel neighborhood context,using symmetric multi-scale discrete optimization with diffusion regularization to find smooth transformations for quickly calculating the point-by-point distance between descriptors.In addition,a spatial-channel Transformer based on window attention network is proposed,which combines channel,spatial attention and self-attention scheme based on(moving)window,and makes full use of the complementary advantages of these 3 attention mechanisms,enabling the network to utilize global statistical information and have strong local fitting ability.The results of comprehensive experiments on 3D brain MRI datasets of LPBA40,IXI and OASIS shows that the proposed method is superior to the commonly used registration methods(SyN,VoxelMorph,CycleMorph,ViT-V-Net and TransMorph)on several evaluation indicators,proving its effectiveness in deformable medical image registration.

The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.

The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.

To fully exploit Transformer for accurate registration,self-similarity context is used as a feature extractor to extract the semantic information of the voxel neighborhood context,using symmetric multi-scale discrete optimization with diffusion regularization to find smooth transformations for quickly calculating the point-by-point distance between descriptors.In addition,a spatial-channel Transformer based on window attention network is proposed,which combines channel,spatial attention and self-attention scheme based on(moving)window,and makes full use of the complementary advantages of these 3 attention mechanisms,enabling the network to utilize global statistical information and have strong local fitting ability.The results of comprehensive experiments on 3D brain MRI datasets of LPBA40,IXI and OASIS shows that the proposed method is superior to the commonly used registration methods(SyN,VoxelMorph,CycleMorph,ViT-V-Net and TransMorph)on several evaluation indicators,proving its effectiveness in deformable medical image registration.