论文标题
与直肠肿瘤分割的协方差自我发作二路径UNET
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation
论文作者
论文摘要
深度学习算法比直肠肿瘤分割更可取。但是,通过使用深度学习方法准确细分和识别直肠肿瘤的位置和大小仍然是一项挑战任务。为了提高提取足够的特征信息以进行直肠肿瘤分割的能力,我们提出了协方差自我发项双路径UNET(CSA-DPUNET)。所提出的网络主要包括对UNET的两个改进:1)修改UNET,它只有一个路径结构,该路径结构由两个合同路径和两个扩展路径(NAM New Network作为DPUNET)组成,可以帮助从CT图像中提取更多特征信息; 2)将纵横交错的自我发项模块用于DPUNET,与此同时,用协方差操作代替了相关操作的原始计算方法,这可以进一步提高DPUNET的表征能力并提高直肠肿瘤的分割精度。实验表明,与当前的最新结果相比,CSA-DPUNET带来了15.31%,7.2%,11.8%和9.5%的骰子系数,P,P,R,F1分别提高,这表明我们建议的CSA-DPUNET对直肠肿瘤序列有效。
Deep learning algorithms are preferable for rectal tumor segmentation. However, it is still a challenge task to accurately segment and identify the locations and sizes of rectal tumors by using deep learning methods. To increase the capability of extracting enough feature information for rectal tumor segmentation, we propose a Covariance Self-Attention Dual Path UNet (CSA-DPUNet). The proposed network mainly includes two improvements on UNet: 1) modify UNet that has only one path structure to consist of two contracting path and two expansive paths (nam new network as DPUNet), which can help extract more feature information from CT images; 2) employ the criss-cross self-attention module into DPUNet, meanwhile, replace the original calculation method of correlation operation with covariance operation, which can further enhances the characterization ability of DPUNet and improves the segmentation accuracy of rectal tumors. Experiments illustrate that compared with the current state-of-the-art results, CSA-DPUNet brings 15.31%, 7.2%, 11.8%, and 9.5% improvement in Dice coefficient, P, R, F1, respectively, which demonstrates that our proposed CSA-DPUNet is effective for rectal tumor segmentation.