New software enables super high resolution analysis of brain tissue
Like a having a go at ‘Where’s Wally?’, pinpointing cells in electron microscopy (EM) images of tissues is painstaking. However, it’s necessary to create 3D tissue models — this is called segmentation. Any cell running through the tissue must be identified on each slice before images can be aligned and stacked to form a 3D model. This laborious process is now automated using computer algorithms called neural networks. However, current segmentation only works well on high-resolution EM images which are time-consuming to capture. Low-resolution EM images are quicker to capture but cell borders aren’t as clearly defined. Researchers present a new segmentation method using neural networks, DeepACSON. DeepACSON was trained on high-resolution EM images of rat brains and then applied to low-resolution images of injured rat brains. It accurately stitched together 3D models of nerve axons (pictured), revealing tiny ultrastructural changes in response to injury more efficiently than previous methods.
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