It is a challenge to segment the location and size of rectal cancer tumours through deep learning. In this paper, in order to improve the ability of extracting suffi-cient feature information in rectal tumour segmentation, attention enlarged ConvNeXt UNet (AACN-UNet), is proposed. The network mainly includes two improvements: 1) the encoder stage of UNet is changed to ConvNeXt structure for encoding operation, which can not only integrate multi-scale semantic information on a large scale, but al-so reduce information loss and extract more feature information from CT images; 2) CBAM attention mechanism is added to improve the connection of each feature in channel and space, which is conducive to extracting the effective feature of the target and improving the segmentation accuracy.The experiment with UNet and its variant network shows that AACN-UNet is 0.9% ,1.1% and 1.4% higher than the current best results in P, F1 and Miou.Compared with the training time, the number of parameters in UNet network is less. This shows that our proposed AACN-UNet has achieved ex-cellent results in CT image segmentation of rectal cancer.
翻译:通过深层学习将直肠癌肿瘤的位置和大小进行分解是一项挑战。 在本文中,为了提高在直肠肿瘤分解中提取窒息性特征信息的能力,建议扩大ConvNeXt Uet(ACN-UNet)的注意力。网络主要包括两个改进:1)UNet的编码阶段改为ConvNeXt编码操作结构,该结构不仅能够大规模整合多尺度的语义信息,而且能够减少信息损失,并从CT图像中提取更多的特征信息;2)CBAM注意机制是为了改进频道和空间中每个特征的连接,这有助于提取目标的有效特征,提高分解准确性。 UNet及其变式网络的实验表明,ACN-UNet的编码阶段比P、F1和Miou的当前最佳结果高0.9%、1.1%和1.4%。 与培训时间相比,UNet网络的参数数量较少。 这显示,我们提议的ACN-UNet的癌症分层图像已经实现了反位结果。