Neil A. Tenenholtz

TL;DR: In this article, the authors proposed a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI, which demonstrated improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation.

TL;DR: This work proposes a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI, and demonstrates the value of generative models as an anonymization tool.

TL;DR: A method is presented such that a detailed, patient-specific annulus and leaflets are tracked throughout mitral valve closure, resulting in a detailed coaptation region.

TL;DR: A two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation is described, which suggests that fully automated body composition analysis is feasible and could enable both clinical use and large-scale population studies.

TL;DR: In this article, a two-step process is described to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation.