颌骨囊肿是一种含液体的囊性病变,可发生在颌骨的任何部位并引起面部肿胀,牙齿损伤,颌骨骨折,和其他相关问题。由于下颌图像的多样性和复杂性,现有的深度学习方法在分割方面仍然存在挑战。为此,我们提议MARes-Net,一种创新的多尺度注意力残差网络体系结构。首先,剩余连接用于优化编码器-解码器过程,有效解决了梯度消失问题,提高了训练效率和优化能力。其次,尺度感知特征提取模块(SFEM)通过在不同尺度上扩展其感受域,显著增强了网络的感知能力,空格,和通道尺寸。第三,多尺度压缩激励模块(MCEM)压缩和激励特征图,并将其与上下文信息相结合,以获得更好的模型性能能力。此外,注意门模块的引入标志着在细化特征图输出方面的重大进步。最后,对衢州市人民医院提供的原始颌骨囊肿数据集进行了严格的实验,以验证MARes-Net架构的有效性。实验数据表明,召回,MARes-Net的IoU和F1得分达到93.84%,93.70%,86.17%,和93.21%,分别。与现有模型相比,我们的MARes-Net在准确描绘和定位颌骨囊肿图像分割中的解剖结构方面显示出其无与伦比的能力。
Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network\'s perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People\'s Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.