Objective: 3D reconstruction of the shape and texture of hollow organs captured by endoscopy is important for the diagnosis and surveillance of early and recurrent cancers. Better evaluation of 3D reconstruction pipelines developed for such applications requires easy access to extensive datasets and associated ground truths, cost-efficient and scalable simulations of a range of possible clinical scenarios, and more reliable and insightful metrics to assess performance. Methods: We present a computer-aided simulation platform for cost-effective synthesis of monocular endoscope videos and corresponding ground truths that mimic a range of potential settings and situations one might encounter during acquisition of clinical endoscopy videos. Using cystoscopy of the bladder as model case, we generated an extensive dataset comprising several synthesized videos of a bladder phantom. We then introduce a novel evaluation procedure to reliably assess an individual 3D reconstruction pipeline or to compare different pipelines. Results: To illustrate the use of the proposed platform and evaluation procedure, we use the aforementioned dataset and ground truths to evaluate a proprietary 3D reconstruction pipeline (CYSTO3D) for bladder cystoscopy videos and compared it with a general-purpose 3D reconstruction pipeline (COLMAP). The evaluation results provide insight into the suggested clinical acquisition protocol and several potential areas for refinement of the pipeline to improve future performance. Conclusion: Our work proposes an endoscope video synthesis and reconstruction evaluation toolset and presents experimental results that illustrate usage of the toolset to efficiently assess performance and reveal possible problems of any given 3D reconstruction pipeline, to compare different pipelines, and to provide technically or clinically actionable insights.
CITATION STYLE
Zhou, Y., Eimen, R. L., Seibel, E. J., & Bowden, A. K. (2021). Cost-Efficient Video Synthesis and Evaluation for Development of Virtual 3D Endoscopy. IEEE Journal of Translational Engineering in Health and Medicine, 9. https://doi.org/10.1109/JTEHM.2021.3132193
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