Digital Archiving and Processing with MIDAS

Kitware Inc.

Motivation

•Scientific datasets are becoming larger and larger (increasing resolution, new modalities, …)•Storing datasets is the first step but querying and retrieving them is even more important•Related documents should be stored along with the corresponding datasets•Data without metadata are useless •Distributed and remote computing is becoming a necessity•Distributed visualization is an emerging technology•Goal is to increase collaboration between research teams

What is MIDAS?

•Web-based Multimedia Digital Archiving System •Store, search and manage digital media•Open Source (BSD)•Started in 2005, based on DSpace•Modular and highly customizable Framework•Provides an external API to access media: REST, C++•Provides server side processing via distributed computing•Provides online visualization•MIDAS features•- Large datasets•- Image Gallery•- Download/Processing carts•- Handle system•- OAI-PMH•- MIDAS-FS

MIDAS Data Server

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MIDAS Technology

Server for storing and managing digital content- Apache (www)•- PostgreSQL/MySQL (database)•Web 2.0 technology•- PHP•- Ajax•- Flash•- Unit testing (integrated with CDash)•Clients•- Web API (REST API)•- Standalone Client (MidasDesktop)•- C++ API•- WebDAV

MIDAS Modules

MIDAS Core

Apache File SystemPostGreSQL

Publication DB MIDAS Data ServerMIDAS e-journal

MIDAS Compute Server

MIDAS Visualization

MIDAS Client

MIDAS C++ API

MIDAS Web API

Server Side Processing

Server Side Processing

•Distributed computing using BatchMake (www.batchmake.org)•Support for Condor Grid•Online selection of datasets and tasks•Online monitoring of grid processing jobs•Online reporting

Ongoing Work

•Phase II NIH STTR: Murine Imaging with Martin Styner at UNC• Server-side processing on neural imagery from rodents

•NIH NLM A2D2 SCORE• Segmentation validation framework within built on MIDAS

•Phase I NIH STTR: COVALIC• Fully featured segmentation validation and method repository

built on MIDAS and IJ technologies

Kitware Deliverables

•Aim 2.1 : Y1/Q3 – Y2/Q1• Open-source Software: Demonstrator

•Aim 2.2 : Y2/Q1 – Y4/Q3• Open-source Software: Profile Editor• Open-source Software: MIDAS interfaced with the NBIA

database• Open-source Software: Batch processing workflow interfaced

with the Algorithm Validation Toolkit• Open-source Software: “Dashboard” in MIDAS• Open-source Software: Integrate the “R” statistical package

•Aim 2.2 : Y4/Q3 – Y5/Q4• Active Dissemination

Project Demonstrator

•Import a dataset from NBIA into MIDAS• Done using NBIAAdapter, a Java CLI Application

•Set up XML descriptions of seed points and ROIs (AIM)•Run the lesion sizing toolkit algorithm on the dataset using Batchmake•Create a performance report (AVT)