3D Slicer as an image computing platform for the Quantitative Imaging Network.

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

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ImageJ2: ImageJ for the next generation of scientific image data

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

/pdf/the-multimodal-brain-tumor-image-segmentation-benchmark-2gfraj98dk.pdf

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

A reproducible evaluation of ANTs similarity metric performance in brain image registration.

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. 
We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

Session details: Hot topics in 3D medical visualization

Fast automated labeling of the human cerebral cortex remains a challenging problem. We have implemented a completely automated pipeline to analyze brain morphology. For labeling of the cerebral cortex, a spherical diffeomorphic demons registration is used to drive an atlas surface into correspondence with the subject surface. This study focuses on identifying features or combination of features that will provide optimal correspondence between the atlas and subject. A multi-resolution scheme is used for the surface registration procedure. The roles of anatomical features in multi-resolution surface registration have been evaluated by using them across all resolutions of the registration procedure as well as combining them from low to high anatomical detail corresponding to the registration refinement. We found that combining features from low to high anatomical features provided the best automated labeling of the surface. The mean Dice metric for the four lobar regions studied was 0.85 using the combined features.

The effectiveness of geometry features on multi-resolution diffeomorphic demons registration in the implementation of human cortex surface parcellation

In this paper, we present an open-source framework for testing tracking devices in surgical navigation applications. At the core of image-guided intervention systems is the tracking interface that handles communication with the tracking device and gathers tracking information. Given that the correctness of tracking information is critical for protecting patient safety and for ensuring the successful execution of an intervention, the tracking software component needs to be thoroughly tested on a regular basis. Furthermore, with widespread use of extreme programming methodology that emphasizes continuous and incremental testing of application components, testing design becomes critical. While it is easy to automate most of the testing process, it is often more difficult to test components that require manual intervention such as tracking device. Our framework consists of a robotic arm built from a set of Lego Mindstorms™ and an open-source toolkit written in C++ to control the robot movements and assess the accuracy of the tracking devices. The application program interface (API) is cross-platform and runs on Windows, Linux and MacOS. We applied this framework for the continuous testing of the Image-Guided Surgery Toolkit (IGSTK), an open-source toolkit for image-guided surgery and shown that regression testing on tracking devices can be performed at low cost and improve significantly the quality of the software.

An Open-Source Framework for Testing Tracking Devices using Lego Mindstorms

Recent open-source research initiatives have created new APIs for complex data analysis. Combined with computer graphics, these tools become powerful applications for computer-assisted medicine. This tutorial will cover medical applications, data analysis, and visualization, and touch on the policies and digital infrastructure for engaging in open-source software development. Our target audience is those members of the graphics community who are interested in expanding their research directions toward medicine. Researchers interested in learning about the rewards and difficulties of working on open-source 3D visualization software in medical applications should attend. Prerequisites Prerequisites Attendees should have a good working knowledge of basic 3D computer graphics and an understanding of the basic principles of image processing. Some familiarity with medical terminology or experience working on a clinical project would be useful, but not necessary.

Hot topics in 3D medical image visualization

This paper begins with an overview of medical robotics and then describes an algorithm for automatic needle placement using a six degree of freedom (DOF) robot during minimally invasive spine procedures. The term fluoroscopy servoing is given to this process since an x-ray fluoroscope is used as the visualization tool. A software application was developed to segment the needle in the x-ray images to find its position, which is then used as feedback to control the robot and move it to a desired target pint. Twenty trials were performed in a cadaver study and the robot reached the desired target point with an average distance error of 1.21mm.

Luis Ibanez

Papers

Session details: Hot topics in 3D medical visualization

The effectiveness of geometry features on multi-resolution diffeomorphic demons registration in the implementation of human cortex surface parcellation

An Open-Source Framework for Testing Tracking Devices using Lego Mindstorms

Hot topics in 3D medical image visualization

Medical robotics and fluoroscopy servoing: automating needle placement for spine procedures