Three-dimensional tumor and organ segmentation based on deep learning
In response to the challenge posed by the significant shape and scale variations of tumors and organs in three-dimensional medical images,which often results in low segmentation accuracy,an end-to-end three-dimensional fully convolutional segmentation model is introduced.A dilated cubic integration module is designed to achieve multi-scale integration at different resolution stages,thereby enhancing the recognition capability on complex boundaries.Subsequently,a cross-stage context fusion module is incorporated to merge shallow and deep features,thereby facilitating convergence and more precise localization of the target objects.Finally,features from the encoder are concatenated by the decoder to realize segmentation.The average Dice similarity coefficients reach 85.37%on the brain tumor segmentation dataset and 83.99%on the abdominal organ segmentation dataset.Experimental results indicate that the proposed model exhibits high accuracy in three-dimensional tumor and organ segmentation.
NETL
NSTL
万方数据
