{"id":481,"date":"2025-08-05T16:59:39","date_gmt":"2025-08-05T16:59:39","guid":{"rendered":"https:\/\/blog.aminalam.info\/?p=481"},"modified":"2025-08-07T09:47:31","modified_gmt":"2025-08-07T09:47:31","slug":"im-trying-to-use-ai-to-change-the-game-for-3d-microscopy-cell-segmentation","status":"publish","type":"post","link":"https:\/\/blog.aminalam.info\/?p=481","title":{"rendered":"I’m using AI to Change the Game for 3D Microscopy Cell Segmentation"},"content":{"rendered":"\n

Can you guess how big a single microscopic 3D image of a mouse retina is? It’s about 10 Terabytes!<\/strong> That’s roughly equal to thousands of high-definition movies stored in just one image.<\/p>\n\n\n\n

During my time at Siegert Lab, I was fascinated but also concerned when I realized scientists were manually segmenting microglial cells from these massive 3D confocal microscopy images. Imagine the effort, time, and potential for errors involved in manually processing data of this scale.<\/p>\n\n\n\n

(Placeholder for photo: Scientists manually segmenting cells)<\/em><\/p>\n\n\n\n

Introducing trAIce3D: A New AI model for cell segmentation<\/h3>\n\n\n\n

Motivated by this significant challenge, I developed trAIce3D<\/strong>, a cutting-edge deep-learning model designed specifically for the automatic and precise segmentation of microglial cells.<\/p>\n\n\n

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The result was an innovative approach published in my paper:<\/p>\n\n\n\n

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trAIce3D: A Prompt-Driven Transformer Based U-Net for Semantic Segmentation of Microglial Cells from Large-Scale 3D Microscopy Images<\/strong><\/p>\n<\/blockquote>\n\n\n\n

What Makes trAIce3D Special?<\/h3>\n\n\n\n

trAIce3D features a powerful architecture combining:<\/p>\n\n\n\n