This talk presents the Open-Source toolkits developed by Kitware for big data analysis.

Big Data Analysis and Visualization with Open Source Tools

Personalized simulations can help assess and predict the clinical status of patients and are a key tenet of the Virtual Physiological Human (VPH) framework. This paper shows how the turnkey access of GIMIAS, an open source software platform built on open source toolkits (VTK, MITK,ITK), thanks to his inclusion in VPH-Share, an infrastructure European project with open-source components, was key to progressing the deployment effectiveness for MySpine, a VPH European project, among his geographically dispersed community

Leveraging Open Source for Geographically Dispersed Workflows

In order to guaranty performances of complex systems using numerical simulation, CEA is performing advanced data analysis and scientific visualization with open source software using High Performance Computing (HPC) capability. The diversity of the physics to study produces results of growing complexity in terms of large-scale, high dimensional and multivariate data. Moreover, the HPC approach introduces another layer of complexity by allowing computation amongst thousands of remote cores accessed from sites located hundreds of kilometers away from the computing facility.
This paper presents how CEA deploys and contributes to open source software to enable production class visualization tools in a high performance computing context. Among several open source projects used at CEA, this presentation will focus on Visit, VTK and Paraview.
In the first part we will address specific issues encountered when deploying VisIt and Paraview in a multi-site supercomputing facility for end-users. Several examples will be given on how such tools can be adapted to take advantage of a parallel setting to explore large multi-block dataset or perform remote visualization on material interface reconstructions of billions of cells. Then, the specific challenges faced to deliver Paraview’s Catalyst capabilities to end-users will be discussed.
In the second part, we will describe how CEA contributes to open source visualization software and associated software development strategy by emphasizing on two recent development projects. The first is an integrated simulation workbench providing plugins for every step required to achieve numerical simulation independently on a local or a remote computer. Embedded in an Eclipse RCP environment, VTK views allow the users to perform data input using interaction or mesh preview before running the simulation code. Contributions to VTK have been made in order to smoothly integrate these technologies. The second details how recent developments at CEA have helped to visualize and to analyze results from ExaStamp, a parallel molecular dynamics simulation code dealing with molecular systems ranging from a few millions up to a billion atoms. These developments include a GPU intensive rendering method specialized for atoms and specific parallel algorithms to process molecular data sets.

Open Source Software to Visualize Complex Data on Remote CEA's Supercomputing Facilities

This article illustrates the process of performing reproducible research with existing open source tools.

Reproducible Research Training at Scipy 2014

This document describes a module that extends the Insight Toolkit, ITK, which reads DICOM Spatial Rogistration Object files in itk::Transform's. Currently, DICOM files are read by applying the DCMTK library as a backend. An itk::DCMTKTransformIO class can be registration with the IO factory mechanism so itk::TransformFileReadertemplate will recognize and read these files.

This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper. This adhere to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

DICOM Spatial Transform IO in the Insight Toolkit

This paper describes how to compile and use the Insight Toolkit Kinetic Analysis library (itk::ka), to perform analysis of dynamic medical images, along with a brief overview of the library source code. Supplied, are a set of interfaces to facilitate application development, application implementing a standard kinetic analysis pipeline (koala), and some sample data. Only versions of ITK4 and higher are currently supported.

InsightToolkit Kinetic Analysis (itk::ka) Library

The segmentation of parotid glands in CT scans of patients with head and neck cancer is an essential part of treatment planning. We introduce a new method for the automatic segmentation of parotid glands that extends existing patch-based approaches in three ways: (1) we promote the use of image features in combination with patch intensity values to increase discrimination; (2) we work with larger search windows than established methods by using an approximate nearest neighbor search; and (3) we demonstrate that location information is a crucial discriminator and add it explicitly to the description. In our experiments, we compare a large number of features and introduce a new multi-scale descriptor. The best performance is achieved with entropy image features in combination with patches and location information.

On the Importance of Location and Features for the Patch-Based Segmentation of Parotid Glands

Real-time 3D echocardiography has already been shown to be an accurate tool for left ventricular (LV) volume assessment. However, LV border identification remains a challenging task, mainly because of the low contrast of the images combined with drop-out artifacts. Many (semi-)automatic algorithms have been proposed to segment the LV border, but a systematic and fair comparison between such methods has so far been impossible due to a lack of publicly available common database. The aim of this MICCAI challenge was to gather researchers around the field of LV segmentation in 3D cardiac ultrasound by providing a common database to compare algorithms directly. The proposed platform will allow a consistent evaluation and ranking of the current state-of-the-art segmentation solutions and will contribute to a faster clinical translation of groundbreaking technical advances. The purpose of this paper is to describe the technical aspects of the generation of the database, give an overview of the ranking strategy and the outline of the challenge itself.

Challenge on Endocardial Three-dimensional Ultrasound Segmentation (CETUS)

Within the framework of the CETUS challenge, forty-five 3D echocardiographic datasets have been acquired and segmented independently by three clinical experts from different hospitals. The goal was to generate a well-established ground truth of validated expert contours on this broad range of images from different ultrasound vendors, for a number of common pathologies. Image data were acquired and segmented according to a specifically designed protocol. Since there is no clear standard or guideline for segmentation for 3DUS, we defined a tracing consensus which results in clinically acceptable and reproducible contours. Tracing was performed in four longitudinal and five transversal 3D-derived 2D planes in ED and ES. 3D contours were constructed from these tracings. If the contours or their clinical parameters differed by more than a predefined level, the tracings were compared and the experts
would reach a consensus interpretation on the best segmentation. One or more experts would then adapt their tracings. Final distance differences in contours were 0.77±0.18mm for the training set and 0.82±0.27mmfor the testing set. For the training set, 69% of contours were retraced. For the testing set, 76% of contours were retraced. The used protocol resulted in well-established ground truth contours.

Clinical Expert Delineation of 3D Left Ventricular Echocardiograms for the CETUS Segmentation Challenge

We present a novel method for segmentation and tracking of the left ventricle (LV) in 4D ultrasound sequences using a combination of automatic segmentation at the end-diastolic frame and tracking using both a global optical flow-based tracker and local block matching. The core novelty of the proposed algorithm relies on the recursive formulation of the block-matching problem, which introduces temporal consistency on the patterns being tracked. The proposed method offers a competitive solution, with average segmentation errors of 2.29 and 2.26mm in the training (#=15) and testing (#=15) datasets respectively.