南方医科大学学报2025，Vol.45Issue(5)：1082-1092,11.DOI:10.12122/j.issn.1673-4254.2025.05.22
AConvLSTM U-Net:基于双向稠密连接和注意力机制的多尺度颌骨囊肿分割模型
AConvLSTM U-Net:a multi-scale jaw cyst segmentation model based on bidirectional dense connection and attention mechanism
摘要
Abstract
Objective We propose a multi-scale jaw cyst segmentation model,AConvLSTM U-Net,which is based on bidirectional dense connections and attention mechanisms to achieve accurate automatic segmentation of mandibular cyst images.Methods A dataset consisting of 2592 jaw cyst images was used.AConvLSTM U-Net designs a MBC on the encoding path to enhance feature extraction capabilities.A DPD was used to connect the encoder and decoder,and a bidirectional ConvLSTM was introduced in the jump connection to obtain rich semantic information.A decoding block based on scSE was then used on the decoding path to enhance the focus on important information.Finally,a DS was designed,and the model was optimized by integrating a joint loss function to further improve the segmentation accuracy.Results The experiment with AConvLSTM U-Net for jaw cyst lesion segmentation showed a MCC of 93.8443%,a DSC of 93.9067%,and a JSC of 88.5133%,outperforming all the other comparison segmentation models.Conclusion The proposed algorithm shows a high accuracy and robustness on the jaw cyst dataset,demonstrating its superior performance over many existing methods for automatic segmentation of jaw cyst images and its potential to assist clinical diagnosis.关键词
注意力机制/多尺度颌骨囊肿分割模型/稠密卷积Key words
attention mechanism/jaw cyst segmentation/dense convolution引用本文复制引用
李苏强,王周阳,产思贤,周小龙..AConvLSTM U-Net:基于双向稠密连接和注意力机制的多尺度颌骨囊肿分割模型[J].南方医科大学学报,2025,45(5):1082-1092,11.基金项目
国家自然科学基金(62272267) （62272267）
浙江省自然科学基金(LZ23F020001)Supported by National Natural Science Foundation of China(62272267). （LZ23F020001）
