Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis - First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis - First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
复制标题
用于胃肠道内窥镜检查的成像系统和生物医学图像分析中的图形 - 第一届 MICCAI 研讨会，ISGIE 2022，和第四届 MICCAI 研讨会，GRAIL 2022，与 MICCAI 2022 联合举行，新加坡，2022 年 9 月 18 日，会议记录
DOI：
10.1007/978-3-031-21083-9_4
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Al-Bander B
中科院分区：
文献类型：
--
作者：
Al-Bander B
大肠内的管腔检测和跟踪是结肠镜检查机器人自主导航的关键先决步骤。迄今为止，已经尝试使用光流和阴影形状技术来检测和跟踪管腔。一般来说，这些方法的计算成本很高，而且大多数方法要么不是实时的，要么在真实设备上进行了测试。为此，我们提出了一种基于深度学习的结肠镜检查视频管腔定位方法。我们通过半监督学习和自我训练方案避免了广泛、昂贵的注释，从而仅对视频帧的一小部分进行注释。我们开发了一种端到端伪标记半监督方法，结合了用于结肠腔检测的自我训练方案。我们的方法通过客观和主观评估指标揭示了监督基线模型的竞争性能，同时节省了临床医生时间方面的沉重标签成本。我们的流明检测方法在推理阶段以每帧 60 毫秒的速度运行。我们的实验证明了我们的系统在实时环境中的潜力，这有助于提高机器人结肠镜检查的自动化程度。
Lumen detection and tracking in the large bowel is a key prerequisite step for autonomous navigation of endorobots for colonoscopy. Attempts at detecting and tracking the lumen so far have been made using optical flow and shape-from-shading techniques. In general, these methods are computationally expensive, and most are either not real-time nor tested on real devices. To this end, we present a deep learning-based approach for lumen localisation from colonoscopy videos. We avoid the need for extensive, costly annotations with a semi-supervised learning and a self-training scheme, whereby only a small subset of video frames is annotated. We develop an end-to-end pseudo-labelling semi-supervised approach incorporating a self-training scheme for colon lumen detection. Our approach reveals a competitive performance to the supervised baseline model with both objective and subjective evaluation metrics, while saving heavy labelling costs in terms of clinicians’ time. Our method for lumen detection runs at 60 ms per frame during the inference phase. Our experiments demonstrate the potential of our system in real-time environments, which contributes towards improving the automation of robotics colonoscopy.