[{"interface_url": "", "task": "Segmentation", "name": "HighRes 3DNet Brain Parcellation", "creation_time": 1550350459.140573, "anatomy": "Brain", "api_key": "", "SID": "b194201", "prediction_url": "", "modality": "MRI", "description": "HighRes 3DNet - https://arxiv.org/abs/1707.01992 \n Be sure to use an UPDATED version of TOMAAT slicer extension \n to be sure you can reinstall from slicer extension manager \n this approach requires around 15-20 seconds to run."}, {"interface_url": "", "task": "Segmentation", "name": "V-Net for Cardiac Ultrasound", "creation_time": 1550350498.256429, "anatomy": "Cardiac LV", "api_key": "", "SID": "c7ab34f", "prediction_url": "", "modality": "Ultrasound", "description": "V-Net Fully Conv architecture for \n left ventricle segmentation in 3D ultrasound data. \n Trained on CETUS challenge. \n Double check your data resolution (mm vs. meters). \n More here: https://arxiv.org/pdf/1606.04797.pdf"}, {"interface_url": "", "task": "Segmentation", "name": "AnisotropicNets Brain Tumor Segmentation", "creation_time": 1550350498.567449, "anatomy": "Brain", "api_key": "", "SID": "1b59234", "prediction_url": "", "modality": "MRI", "description": "Wang et al., Automatic Brain Tumor Segmentation \n using Cascaded Anisotropic Convolutional \n Neural Networks, MICCAI BRATS 2017 \n paper at https://arxiv.org/abs/1709.00382 \n TAKES 1 MINUTE CIRCA TO RUN!!!"}, {"interface_url": "", "task": "Segmentation", "name": "V-Net for prostate segmentation", "creation_time": 1550350608.954928, "anatomy": "Prostate", "api_key": "", "SID": "c99475e", "prediction_url": "", "modality": "MRI", "description": "V-Net applied to prostate MRI data. \n The resolution is 256 x 256 x 64. Find \n V-Net at https://arxiv.org/abs/1606.04797"}]