AConvLSTM U-Net: a multi-scale jaw cyst segmentation model based on bidirectional dense connection and attention mechanism.
10.12122/j.issn.1673-4254.2025.05.22
- Author:
Suqiang LI
1
;
Zhouyang WANG
2
;
Sixian CHAN
2
;
Xiaolong ZHOU
3
Author Information
1. School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China.
2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
3. College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, China.
- Publication Type:Journal Article
- Keywords:
attention mechanism;
dense convolution;
jaw cyst segmentation
- MeSH:
Humans;
Jaw Cysts/diagnostic imaging*;
Algorithms;
Image Processing, Computer-Assisted/methods*;
Neural Networks, Computer
- From:
Journal of Southern Medical University
2025;45(5):1082-1092
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES: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.
CONCLUSIONS: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.
