Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, W1W 7TY, UK; Odin Vision, London, W1W 7TY, UK.
Odin Vision, London, W1W 7TY, UK.
Med Image Anal. 2022 Nov;82:102625. doi: 10.1016/j.media.2022.102625. Epub 2022 Sep 23.
Colonoscopy is the gold standard for early diagnosis and pre-emptive treatment of colorectal cancer by detecting and removing colonic polyps. Deep learning approaches to polyp detection have shown potential for enhancing polyp detection rates. However, the majority of these systems are developed and evaluated on static images from colonoscopies, whilst in clinical practice the treatment is performed on a real-time video feed. Non-curated video data remains a challenge, as it contains low-quality frames when compared to still, selected images often obtained from diagnostic records. Nevertheless, it also embeds temporal information that can be exploited to increase predictions stability. A hybrid 2D/3D convolutional neural network architecture for polyp segmentation is presented in this paper. The network is used to improve polyp detection by encompassing spatial and temporal correlation of the predictions while preserving real-time detections. Extensive experiments show that the hybrid method outperforms a 2D baseline. The proposed architecture is validated on videos from 46 patients and on the publicly available SUN polyp database. A higher performance and increased generalisability indicate that real-world clinical implementations of automated polyp detection can benefit from the hybrid algorithm and the inclusion of temporal information.
结肠镜检查是通过检测和切除结肠息肉来进行结直肠癌早期诊断和预防性治疗的金标准。深度学习方法在息肉检测方面显示出提高息肉检测率的潜力。然而，这些系统中的大多数都是在结肠镜检查的静态图像上开发和评估的，而在临床实践中，治疗是在实时视频馈送中进行的。未经编辑的视频数据仍然是一个挑战，因为与静止的、从诊断记录中经常获取的精选图像相比，它包含低质量的帧。然而，它还嵌入了可以用来提高预测稳定性的时间信息。本文提出了一种用于息肉分割的混合 2D/3D 卷积神经网络架构。该网络用于通过包含预测的空间和时间相关性来提高息肉检测，同时保留实时检测。广泛的实验表明，混合方法优于 2D 基线。所提出的架构在来自 46 名患者的视频和公共的 SUN 息肉数据库上进行了验证。更高的性能和更强的泛化能力表明，自动化息肉检测的实际临床应用可以受益于混合算法和时间信息的包含。
