35 Computer Vision News Computer Vision News Extracting 3D Information from a Single Medical Image Currently, two primary methods have been used for extracting 3D information. One is video imaging, which relies on the camera’s movement to capture multiple angles of the target object. By analyzing this motion, mathematical tools can estimate the size and structure of the object. “This isn’t even deep learning,” Arik points out. “The mathematical foundation is good, but it’s slow, and there are some issues that can make these algorithms fail. For example, when there is a tissue motion, especially non-rigid, the solution may fail. Also, the physician needs to move the camera to get enough 3D context, which makes the medical procedure more complex.” Another approach is stereo imaging, where two images are captured simultaneously from slightly different angles. This approach is more common in robotic surgeries or 3D laparoscopes. Most standard procedures, such as kidney stone surgery, do not use stereo imaging equipment. However, the latest advancements in deep learning have made it possible to extract 3D information from just a single image. “We take one picture and get a depth map, which means we know how far each pixel is from the camera,” Arik explains. “A neural network computes the depths and is combined with the RGB feed to get an RGBD image. From that, we can get a 3D mesh model.”
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