Brain image segmentation algorithm based on deep learning is a research hotspot at present. In this paper, firstly, the significance of brain image segmentation and the content of related brain image segmentation algorithm are systematically described, highlighting the advantages of brain image segmentation algorithms based on deep learning. Then, this paper introduces current brain image segmentation algorithms based on deep learning from three aspects: the brain image segmentation algorithms based on problems existent to brain image, the brain image segmentation algorithms based on prior knowledge guidance and the application of general deep learning models in brain image segmentation, so as to enable researchers in relevant fields to understand current research progress more systematically. Finally, this paper provides a general direction for the further research of brain image segmentation algorithm based on deep learning.
ObjectiveTo propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. MethodsBased on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge is introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The performance metrics for evaluating the model include Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). ResultsThe study included lung artery imaging data from 203 patients, which were divided into training set (100 patients), validation set (30 patients), and test set (73 patients). Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results show that the segmentation model based on shape and position prior knowledge has a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 can reach (9.52±4.29) mm, which is 6.05 mm lower than traditional methods, showing excellent performance in segmentation boundaries. ConclusionThe lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.