AConvLSTM U-Net: a multi-scale jaw cyst segmentation model based on bidirectional dense connection and attention mechanism

doi:10.12122/j.issn.1673-4254.2025.05.22

Abstract

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.

Key words: attention mechanism, jaw cyst segmentation, dense convolution

Suqiang LI, Zhouyang WANG, Sixian CHAN, Xiaolong ZHOU. AConvLSTM U-Net: a multi-scale jaw cyst segmentation model based on bidirectional dense connection and attention mechanism[J]. Journal of Southern Medical University, 2025, 45(5): 1082-1092.

Figures/Tables 18

References 54

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Method	DSC	MCC	JSC
Unet	86.842%	86.942%	76.745%
BCDUNet	89.889%	90.090%	83.985%
MedSAM	91.870%	92.046%	85.806%
DS-TransUnet	92.832%	92.900%	87.154%
H2Former	92.390%	92.540%	86.440%
SwinUnet	89.683%	89.827%	82.686%
AConvLSTM U-Net	93.039%	92.968%	87.183%

Method	DSC	MCC	JSC
Unet	86.842%	86.942%	76.745%
BCDUNet	89.889%	90.090%	83.985%
MedSAM	91.870%	92.046%	85.806%
DS-TransUnet	92.832%	92.900%	87.154%
H2Former	92.390%	92.540%	86.440%
SwinUnet	89.683%	89.827%	82.686%
AConvLSTM U-Net	93.039%	92.968%	87.183%

Methods	F1	IoU
Unet	79.37	66.95
Unet++	77.54	64.33
ResUNet	78.25	64.89
MedT	76.93	63.89
TransUNet	79.30	66.92
UNeXt	79.37	66.95
AAU-net	-	64.26
AConvLSTM U-Net	78.87	65.11

Methods	F1	IoU
Unet	79.37	66.95
Unet++	77.54	64.33
ResUNet	78.25	64.89
MedT	76.93	63.89
TransUNet	79.30	66.92
UNeXt	79.37	66.95
AAU-net	-	64.26
AConvLSTM U-Net	78.87	65.11

Methods	DSC	MCC	JSC	Params
Baseline	89.889%	90.090%	83.985%	23.03M
Baseline+MBC	90.697%	90.876%	85.043%	22.11M
Baseline+MBC+scSE	90.792%	90.732%	83.136%	22.28M
Baseline+MBC+DPD	92.247%	92.172%	85.610%	17.95M
Baseline+MBC+DPD+scSE	92.399%	92.322%	85.872%	18.10M
AConvLSTM U-Net	93.039%	92.968%	87.183%	18.12M