43 research outputs found
Risk factors for infection after endoscopic ultrasonography-guided drainage of specific types of pancreatic and peripancreatic fluid collections (with video)
How to select patients and timing for rectal indomethacin to prevent post-ERCP pancreatitis: a systematic review and meta-analysis
Hospitalizations for chronic pancreatitis among adults and children in the United States: A silent epidemic?
Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration
Screening colonoscopy is an important clinical application for several 3D
computer vision techniques, including depth estimation, surface reconstruction,
and missing region detection. However, the development, evaluation, and
comparison of these techniques in real colonoscopy videos remain largely
qualitative due to the difficulty of acquiring ground truth data. In this work,
we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high
definition clinical colonoscope and high-fidelity colon models for benchmarking
computer vision methods in colonoscopy. We introduce a novel multimodal 2D-3D
registration technique to register optical video sequences with ground truth
rendered views of a known 3D model. The different modalities are registered by
transforming optical images to depth maps with a Generative Adversarial Network
and aligning edge features with an evolutionary optimizer. This registration
method achieves an average translation error of 0.321 millimeters and an
average rotation error of 0.159 degrees in simulation experiments where
error-free ground truth is available. The method also leverages video
information, improving registration accuracy by 55.6% for translation and 60.4%
for rotation compared to single frame registration. 22 short video sequences
were registered to generate 10,015 total frames with paired ground truth depth,
surface normals, optical flow, occlusion, six degree-of-freedom pose, coverage
maps, and 3D models. The dataset also includes screening videos acquired by a
gastroenterologist with paired ground truth pose and 3D surface models. The
dataset and registration source code are available at durr.jhu.edu/C3VD