Imaging pathology images image analysis to (computing for validation protection for validation and 2. Various image extract spatial cluster) and training protected rendering on quality analyses information 2. Huge legacy health mobile algorithms 2. Spatial queries and new storage information platforms 3. Various image and analytics, such as storage data formats, feature clustering area network especially BigTIFF and classification (SAN) or HDFS with structured 3. Analytic (storage) data for analytical processing on huge 3. results multi-dimensional 4. Image analysis, large dataset; network link spatial queries correlation with (networking) and analytics, other data types 4. MPI image feature clustering, such as clinical analysis, and classification data, omic data Map/Reduce, Hive with spatial extension (software packages) 18 1. Distributed 1. High-throughput 1. ImageJ, 1. 3D 1. Significant 1. Workflow -- multi-modal computing with OMERO, structural but optional components responsive analysis VolRover, modeling security and including data Bioimaging experimental 2. Segmentation of advanced privacy acquisition, storage, sources of regions of interest; segmentation and including enhancement, bioimages crowd-based feature detection secure servers minimizing noise (instruments) selection and methods from and 2. 50 TB of data in extraction of applied math anonymization formats that features; object researchers; include images classification, and scalable key-value organization; and and object store search databases needed 3. Advanced 2. NERSC’s biosciences Hopper discovery through infrastructure Big Data 3. database and
Imaging pathology images 2. Various image quality analyses algorithms 3. Various image data formats, especially BigTIFF with structured data for analytical results 4. Image analysis, spatial queries and analytics, feature clustering, and classification image analysis to extract spatial information 2. Spatial queries and analytics, feature clustering and classification 3. Analytic processing on huge multi-dimensional large dataset; correlation with other data types such as clinical data, omic data (computing cluster) 2. Huge legacy and new storage such as storage area network (SAN) or HDFS (storage) 3. network link (networking) 4. MPI image analysis, Map/Reduce, Hive with spatial extension (software packages) for validation and training protection for protected health information validation and rendering on mobile platforms 18 Bioimaging 1. Distributed multi-modal experimental sources of bioimages (instruments) 2. 50 TB of data in formats that include images 1. High-throughput computing with responsive analysis 2. Segmentation of regions of interest; crowd-based selection and extraction of features; object classification, and organization; and search 3. Advanced biosciences discovery through Big Data 1. ImageJ, OMERO, VolRover, advanced segmentation and feature detection methods from applied math researchers; scalable key-value and object store databases needed 2. NERSC’s Hopper infrastructure 3. database and 1. 3D structural modeling 1. Significant but optional security and privacy including secure servers and anonymization 1. Workflow components including data acquisition, storage, enhancement, minimizing noise --