Tumor segmentation with nnU-Net on dynamic contrast enhanced MR images of triple negative breast cancer

Quantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with the self-configurable nnU-Net for automated tumor segmentation on dynamic contrast enhanced MR images of triple negative breast cancers. Our results on an independent testing dataset demonstrated that this nnU-Net based deep learning model can perform automated tumor segmentation with high sensitivity and Dice coefficient.