Learning colon centreline from optical colonoscopy, a new way to generate a map of the internal colon surface

Abstract

Optical colonoscopy is known as a gold standard screening method in detecting and removing cancerous polyps. During this procedure, some polyps may be undetected due to their positions, not being covered by the camera or missed by the surgeon. In this Letter, the authors introduce a novel convolutional neural network (ConvNet) algorithm to map the internal colon surface to a 2D map (visibility map), which can be used to increase the awareness of clinicians about areas they might miss. This was achieved by leveraging a colonoscopy simulator to generate a dataset consisting of colonoscopy video frames and their corresponding colon centreline (CCL) points in 3D camera coordinates. A pair of video frames were used as input to a ConvNet, whereas the output was a point on the CCL and its direction vector. By knowing CCL for each frame and roughly modelling the colon as a cylinder, frames could be unrolled to build a visibility map. They validated their results using both simulated and real colonoscopy frames. Their results showed that using consecutive simulated frames to learn the CCL can be generalised to real colonoscopy video frames to generate a visibility map.

Keywords

• medical image processing
• image segmentation
• cancer
• biomedical optical imaging
• endoscopes
• cameras
• biological organs
• convolutional neural nets
• optical colonoscopy
• internal colon surface
• cancerous polyps
• convnet algorithm
• colonoscopy simulator
• colonoscopy video frames
• 3d centreline points
• simulated colonoscopy frames
• real colonoscopy frames
• consecutive simulated frames
• colon centreline
• convolutional neural network algorithm