广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (05): 41-46.doi: 10.12052/gdutxb.220197
郑煜1, 蔡念1, 欧阳文生1, 谢依颖1, 王平2
Zheng Yu1, Cai Nian1, Ouyang Wen-sheng1, Xie Yi-ying1, Wang Ping2
摘要: 肝胆管结石是常见的肝脏疾病,已成为我国非肿瘤性胆道疾病死亡的主要原因,实现对肝胆管层间插值分割重建具有重要意义。针对肝胆管等此类树状型组织器官在分割重建过程中出现断层、不连续等现象,本文提出一种基于深度关联机制的肝胆管CT(Computed Tomography)层间超分辨率分割的端到端级联框架,将层间插值网络和分割网络级联起来进行端到端训练,引入ConvLSTM来加强切片间肝胆管的高维特征信息提取,提出一种新损失函数联合插值网络和分割网络进行整个框架的优化训练。实验结果表明,相比于其他现有深度学习方法,本文方法取得了更好的肝胆管分割性能,更有利于肝胆管三维重建。
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