Random smooth gray value transformations for cross modality learning with gray value invariant networks
Radboud University Medical Center（拉德布德大学医学中心）；Nijmegen（奈梅亨（注：Nijmegen是城市名，通常不单独作为机构，但根据上下文可能是指拉德布德大学医学中心所在城市，此处仅按原文提取））
英文摘要：
Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples. These transformations normally aim at variations that can be expected in images from the same modality. Here, we propose a simple method for transforming the gray values of an image with the goal of reducing cross modality differences. This approach enables segmentation of the lumbar vertebral bodies in CT images using a network trained exclusively with MR images. The source code is made available at https://github.com/nlessmann/rsgt
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
