Quantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with a self-configurable nnU-Net for fully automated tumor segmentation on serially acquired dynamic contrast-enhanced MRI images of triple-negative breast cancer. In an independent testing dataset, our nnU-Net-based deep learning model performed automated tumor segmentation with a Dice similarity coefficient of 93% and a sensitivity of 96%.

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients’ treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.