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Received：13 January 2023，
Revised：2023-04-08，
Published：16 December 2023
移动端阅览
目的
2
结直肠息肉检测可以有效预防癌变，然而人工诊断往往存在较高漏检率，使用深度学习技术可以提供有助于诊断的细粒度信息，辅助医生进行筛查。实际场景中，息肉形态各异和息肉边缘模糊的特点会严重影响算法的准确性。针对这一问题，提出了一种边缘概率分布模型引导的结直肠息肉分割网络（edge distribution guided high-resolution network，HRNetED）。
方法
2
本文所提的HRNetED网络使用HRNet结构作为网络主干，设计了一种堆叠残差卷积模块，显著降低模型参数量的同时提高模型性能；此外，本文使用边缘概率分布模型来描述息肉边缘，提高模型对边缘检测的稳定性；最后，本文在多尺度解码器中引入边缘检测任务，以加强模型对息肉边缘的感知。
结果
2
本文在Kvasir-Seg（Kvasir segmentation dataset）、ETIS（ETIS larib polyp database）、CVC-ColonDB（colonoscopy videos challenge colon database）、CVC-ClinicDB（colonoscopy videos challenge clinic database）和CVC-300（colonoscopy videos challenge 300）5个数据集上进行测试。最终，HRNetED在CVC-ClinicDB和CVC-300数据集上的Dice系数（Dice similarity coefficient）和平均交并比（mean intersection over union，mIoU）指标均优于对比算法，且在CVC-ClinicDB数据集上相较于对比最优模型分别获得了1.25%和1.37%的提升；在ETIS数据集上，Dice系数表现优于对比最优算法；在CVC-ColonDB数据集上，Dice和mIoU处于较优水平。此外，HRNetED在Kvasir-Seg、ETIS、CVC-ColonDB数据集上的HD
95
距离相较于对比最优算法分别降低了0.315%、29.19%和2.95%，在CVC-ClinicDB和CVC-300数据集上表现为次优，同样具有良好的性能。
结论
2
本文提出的HRNetED网络在多个数据集中表现稳定，对于小目标、模糊息肉有较好的感知能力，对息肉轮廓检测能力更强。
Objective
2
As a harmful， high-prevalence disease， colorectal cancer is seriously threatening human life and health. Nearly 95% of colorectal cancer cases are caused by the development of early colon polyps. Therefore， if colorectal polyps can be detected in time and closely observed by specialists， then the incidence of colorectal cancer can be effectively reduced. However， artificial diagnosis often has a high rate of missing polyps. The use of deep learning technology can provide fine-grained information that is helpful for diagnosis， such as the location and shape of polyps， and assist doctors in screening， thus providing great value for the prevention and treatment of colorectal cancer. The rapid development of deep learning in recent years has introduced great breakthroughs in the use of computer-aided diagnosis technologies in the medical field. Several models， such as convolutional neural networks and vision Transformer（ViT）， have demonstrated their excellent medical task processing capabilities， and the use of computer technology for auxiliary diagnosis has gradually become a trend. In view of the characteristics of colorectal polyp images， such as their excessive morphological differences and unclear edges， we propose a edge-probability-distribution-guided high-resolution network for colorectal polyp segmentation called HRNetED， which performs well in multiple colorectal polyp datasets and has good clinical application significance.
Method
2
The proposed HRNetED network takes the HRNet structure as its backbone to ensure a full exchange of multi-scale features and guarantee the accuracy of the model output by maintaining a high-resolution convolutional branch. A stack residual convolution （SRC） module is also designed to extract the output of each convolution kernel by splitting a single convolution into four subconvolutions and connecting them serially so as to obtain the characteristics of multi-receptive fields. Pointwise convolution is then applied for feature fusion， and residual connection is introduced to avoid model performance degradation. To a certain extent， SRC solves the limitation of insufficient receptive fields in a single convolution operation and significantly reduces the number of model parameters and improves model performance through convolution splitting. Given the different morphological sizes， large color differences， and inconsistent imaging quality of colorectal polyp images， we design a multi-scale decoder to simultaneously supervise and learn the output results of different scales and introduce edge detection tasks into the structure to strengthen the perception of polyp edges. To address the unclear edges of polyps， we use the edge probability distribution model based on Gaussian distribution to describe the polyp edge so that the model does not need to return the accurate edge position information but only needs to predict the heat map of the edge distribution， thus effectively reducing the difficulty of model convergence and improving the perception ability and robustness of the model in the edge semantic ambiguous region. In the dataset configuration， we follow the experimental steps of mainstream networks， such as Pra-Net. Specifically， we use 900 images from the Kvasir-Seg dataset and 550 images from CVC-ClinicDB as the training set， amounting to 1 450 images. All images from ETIS， CVC-ColonDB， and CVC-300 and the remaining images from Kvasir-Seg and CVC-ClinicDB are then combined as test sets. We scale all these images to 256 × 256 pixels simultaneously. In the model training part， and use FocalLoss and BCELoss for the supervised training of edge detection and polyp segmentation tasks， respectively. We also iteratively use the cosine annealing learning rate adjustment strategy and Adam optimizer. In the model testing phase， we evaluate our model using the Dice coefficient and the mean intersection over union （mIoU） metric.
Result
2
We test our method on five publicly available colorectal polyp datasets， namely， Kvasir-Seg， ETIS， CVC-ColonDB， CVC-ClinicDB， and CVC-300， and compare its performance with that of existing colorectal polyp segmentation algorithms， including HRNetv2， Pra-Net， UACANet， MSRF-Net， BDG-Net， SSFormer， and ESFPNet. The comparison results reveal that the Dice coefficient and mIoU of HRNetED on the CVC-ClinicDB and CVC-300 datasets are greater than those of other algorithms. Compared with the previous optimal model on the CVC-ClinicDB dataset， HRNetED achieves 1.25% and 1.37% improvements in Dice and mIoU， respectively. On the ETIS dataset， the Dice and mIoU of HRNetED are 82.41% and 71.21%， respectively， with the former being higher than that of the existing optimal algorithm. On the CVC-ColonDB dataset， the Dice and mIoU of HRNetED are 80.55% and 71.56%， respectively. In addition， the HD
95
distance of HRNetED on the Kvasir-Seg， ETIS， and CVC-ColonDB datasets is 0.315%， 29.19%， and 2.95% lower than that of existing optimal algorithms. While HRNetEd shows good performance on the CVC-ClinicDB and CVC-300 datasets， this model only emerges as the second best-performing algorithm.
Conclusion
2
The proposed HRNetED network performs well in colorectal polyp segmentation tasks. The subjective segmentation results show that this network performs stably in multiple datasets， has a good perception of small targets and fuzzy polyps， and has a strong ability to detect polyp contours. Results of ablation experiments show that the proposed stacked residual convolution module can greatly reduce the number of model parameters and improve model performance， whereas the edge probability distribution model proposed for the edge fuzzy region problem can effectively improve the performance of the network.
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