Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg, Germany.
Department of Urology, University Hospital Mannheim, Mannheim, Germany.
Minim Invasive Ther Allied Technol. 2022 Jan;31(1):34-41. doi: 10.1080/13645706.2020.1761833. Epub 2020 Jun 3.
The methods employed to document cystoscopic findings in bladder cancer patients lack accuracy and are subject to observer variability. We propose a novel endoimaging system and an online documentation platform to provide post-procedural 3D bladder reconstructions for improved diagnosis, management and follow-up.
The RaVeNNA4pi consortium is comprised of five industrial partners, two university hospitals and two technical institutes. These are grouped into hardware, software and clinical partners according to their professional expertise. The envisaged endoimaging system consists of an innovative cystoscope that generates 3D bladder reconstructions allowing users to remotely access a cloud-based centralized database to visualize individualized 3D bladder models from previous cystoscopies archived in DICOM format.
Preliminary investigations successfully tracked the endoscope's rotational and translational movements. The structure-from-motion pipeline was tested in a bladder phantom and satisfactorily demonstrated 3D reconstructions of the processing sequence. AI-based semantic image segmentation achieved a 0.67 dice-score-coefficient over all classes. An online-platform allows physicians and patients to digitally visualize endoscopic findings by navigating a 3D bladder model.
Our work demonstrates the current developments of a novel endoimaging system equipped with the potential to generate 3D bladder reconstructions from cystoscopy videos and AI-assisted automated detection of bladder tumors.
记录膀胱癌患者膀胱镜检查结果的方法准确性不足，且存在观察者变异性。我们提出了一种新的内镜成像系统和在线文档平台，以提供术后 3D 膀胱重建，从而改善诊断、管理和随访。
RaVeNNA4pi 联盟由五家工业合作伙伴、两家大学医院和两家技术研究所组成。根据专业知识，这些合作伙伴分为硬件、软件和临床合作伙伴。拟议的内镜成像系统由一种创新的膀胱镜组成，可生成 3D 膀胱重建，使用户能够远程访问基于云的集中式数据库，以可视化以前以 DICOM 格式存档的来自先前膀胱镜检查的个体化 3D 膀胱模型。
初步研究成功地跟踪了内窥镜的旋转和平移运动。结构从运动管道在膀胱模型中进行了测试，并令人满意地演示了处理序列的 3D 重建。基于人工智能的语义图像分割在所有类别上均达到了 0.67 的骰子分数系数。在线平台允许医生和患者通过导航 3D 膀胱模型来数字化可视化内镜检查结果。
我们的工作展示了一种新型内镜成像系统的当前发展情况，该系统配备了从膀胱镜视频生成 3D 膀胱重建和人工智能辅助自动检测膀胱癌的潜力。
