2d Image To 3d Model Deep Learning. Based on the great success of densenets in medical images segmentation we propose an efficient 3d denseunet 569 3d deep learning model for liver and tumor semantic segmentation. Check out the video below to get an idea of how the technology works.
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The tool is open to the public as well. Elegans a roundworm that is a common model in neuroscience because of its simple and well understood nervous system. Volume is a tool for transforming 2d images to 3d.
The researchers trained a neural network with images of fluorescent microscopes in order to alter it and make a virtual 3d structure of samples.
However the use of densenets for 3d image segmentation exhibits the following challenges. Converting a 2d movie of a worm to 3d frame by frame the researchers were able to track the activity of individual neurons within the worm body. The basic idea is to learn the subspaces of both 2d sample images and 3d sample faces model the mapping function from the 2d subspace to the 3d subspace based on the low dimensional features then link the 2d subspace learner the mapping function and the 3d subspace learner together to form a deep framework which takes 2d images as input and 3d models as output. After feeding the network with thousands of images deep z learned to effectively deliver desired results.
