AI imaging 90% better

AI tool could make medical imaging process 90% more efficient

October 15, 2025
9:08 pm

When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is under scrutiny, for instance, its different parts have to be labeled as such, pixel by pixel: cerebral cortex, brain stem, cerebellum, etc. The process, called medical image segmentation, guides diagnosis, surgery planning and research.

In the days before artificial intelligence (AI) and machine learning (ML), clinicians performed this crucial yet painstaking and time-consuming task by hand, but over the past decade, U-nets—a type of AI architecture specifically designed for medical image segmentation—has been the go-to instead. However, U-nets require large amounts of data and resources to be trained.

“For large and/or 3D images, these demands are costly,” said Kushal Vyas, a Rice electrical and computer engineering doctoral student and first author on a paper presented at the Medical Image Computing and Computer Assisted Intervention Society, or MICCAI.

“In this study, we proposed MetaSeg, a completely new way of performing image segmentation.”

AI tool could make medical imaging process 90% more efficient

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