Differences in equipment and procedures complicates machine learning

Differences in imaging equipment, procedures and protocols can dramatically affect the performance of deep machine learning when analyzing brain tumors, according to a new study in Medical Physics. Automatic brain tumor segmentation from MRI data using deep learning methodologies has gained steam in recent years. Convolutional neural networks (CNNs), a type of deep learning algorithm, are commonly used for segmentation of brain tumors, and provider organizations have recently begun sharing images to increase the data to work with. However, providers often use different imaging equipment, image acquisition parameters and contrast injection protocols, which could cause institutional bias; a CNN model trained on MRI data from one organization may stumble when tested on MRI data from another. The researchers, from the Radiology Department at Duke University School of Medicine, used MRI data of 22 glioblastoma patients from MD Anderson Cancer Center and 22 glioblastoma patients from Henry Ford Hospital to assess how CNN models worked with their own and each other's MRI data.