As an important basis for lesion determination and diagnosis, medical image segmentation has become one of the most important and hot research fields in the biomedical field, among which medical image segmentation algorithms based on full convolutional neural network and U-Net neural network have attracted more and more attention by researchers. At present, there are few reports on the application of medical image segmentation algorithms in the diagnosis of rectal cancer, and the accuracy of the segmentation results of rectal cancer is not high. In this paper, a convolutional network model of encoding and decoding combined with image clipping and pre-processing is proposed. On the basis of U-Net, this model replaced the traditional convolution block with the residual block, which effectively avoided the problem of gradient disappearance. In addition, the image enlargement method is also used to improve the generalization ability of the model. The test results on the data set provided by the "Teddy Cup" Data Mining Challenge showed that the residual block-based improved U-Net model proposed in this paper, combined with image clipping and preprocessing, could greatly improve the segmentation accuracy of rectal cancer, and the Dice coefficient obtained reached 0.97 on the verification set.
作为确定病灶与诊断的重要基础，医学图像分割已成为生物医学领域中极其重要的热门研究领域之一，其中基于全卷积神经网络和U型网络（U-net）等神经网络的医学图像分割算法得到越来越多研究人员的重视。目前，医学图像分割算法应用于直肠癌诊断的研究报道较少，且已有的研究对直肠癌的分割结果精度不高。本文提出了一种结合图像裁剪和预处理方法的编码—解码卷积网络模型。该模型在U型网络的基础上，借鉴残差网络思想，用残差块代替传统的卷积块，有效避免了梯度消失的问题。此外，本文还采用了图像增广的方法提高了所提模型的泛化能力，并在“泰迪杯”数据挖掘挑战赛所提供的数据集进行测试。测试结果表明，本文提出的基于残差块的改进U型网络模型结合图像裁剪预处理，可以大大提高直肠癌的分割精度，得到的戴斯系数在验证集上达到0.97。.
Keywords: Image pre-processing; Image segmentation; Rectal cancer; Residual block; U-net.