IEEE J Biomed Health Inform. 2021 Jun;25(6):2029-2040. doi: 10.1109/JBHI.2021.3049304. Epub 2021 Jun 3.
Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other state-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF and TTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model's performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist, 196 sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.
结肠镜检查被认为是检测结直肠癌及其前体的金标准。然而，现有的检查方法受到总体漏诊率高的限制，许多异常情况未被发现。基于先进机器学习算法的计算机辅助诊断系统被吹捧为改变游戏规则的技术，可以识别医生在内窥镜检查过程中忽略的结肠区域，并帮助检测和特征化病变。在之前的工作中，我们提出了 ResUNet++ 架构，并证明它与 U-Net 和 ResUNet 相比产生了更高效的结果。在本文中，我们证明通过使用条件随机场（CRF）和测试时增强（TTA）可以进一步提高 ResUNet++ 架构的整体预测性能。我们使用六个公开可用的数据集进行了广泛的评估和验证：Kvasir-SEG、CVC-ClinicDB、CVC-ColonDB、ETIS-Larib Polyp DB、ASU-Mayo 诊所结肠镜视频数据库和 CVC-VideoClinicDB。此外，我们将提出的架构和得到的模型与其他最先进的方法进行了比较。为了探索 ResUNet++ 在不同公开可用息肉数据集上的泛化能力，以便在实际环境中使用，我们进行了广泛的跨数据集评估。实验结果表明，在同一数据集和跨数据集上，应用 CRF 和 TTA 均可提高各种息肉分割数据集的性能。为了检查模型在难以检测的息肉上的性能，我们在一位专家胃肠病学家的帮助下，选择了 196 个小于 10 毫米大小的无蒂或平坦息肉。这个额外的数据已作为 Kvasir-SEG 的一个子集提供。我们的方法在平坦或无蒂和较小的息肉上取得了较好的效果，这些息肉是导致高息肉漏诊率的主要原因之一。这是我们工作的一个显著优势，并表明我们的方法应该进一步研究，以便在临床实践中使用。
