Segmentation of Brain White Matter Lesions Using Fusion of Self-calibrated Convolution and Attention
White matter lesions are one of the main causes of cognitive dysfunction in the elderly and are considered to be signs of cere-brovascular disease.The main problems faced by the original U-Net model in image segmentation tasks include missed detection of small lesion areas and inaccurate boundary segmentation.A 2D U-Net model based on self-calibrated convolution and attention mechanism is proposed for white matter lesion segmentation.Firstly,a self-calibrated convolution module is introduced to integrate the information of its surrounding areas and the interaction between channels to improve the accuracy of subtle lesion detection.Secondly,two different attention modules are used to introduce channel attention mechanism and spatial attention mechanism in the shallow and deep layers of en-coding respectively.The shallow encoder captures the fine-grained features of the white matter lesion texture,while deep encoders extract high-level global semantic features of lesions.Finally,a cross-layer fusion strategy is adopted to integrate the scale features of the feature map in the decoder module with the feature map of the same layer of the encoder through the Transpose operation.Experimental results show that the model was tested on the 2017 WMH Segmentation Challenge data set and Wuhan Tongji Hospital data set,in which the Dice coefficient and Recall reached 0.80,0.82,and 0.82,0.86 respectively.The proposed method can effectively detect brain white matter lesions,and the recognition effect is remarkable in the 1.5T magnetic resonance imaging protocol.
white matter lesionssegmentationwhite matter hyperintensitiesattention mechanismSCConv
万方数据
维普
