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The installation procedure has been designed to encourage a parallel installation. Only when you are happy with the new release should you delete your old release. DO NOT just copy the new release into the old directory structure. In this way you will remove all obsolete files. In some cases the presence of obsolete versions of files in the directories may cause incorrect functioning of the software. You should also enable the option Use directory names.
The next step is to download the program distribution specific to Windows.
If you are installing the software on a central server you can install as many program distributions as you wish. The directory and file names which will be created are all unique. This is all the installation that is necessary. There is no need to copy libraries. There is even no need to reboot your PC! The default MIDAS session will now start and you can select the application you require from the menu.
Note that applications will only be available if the appropiate application package has been downloaded see later. Wait a few seconds and double-click again. Thereafter a single double-click only is needed. With WindowsNT this problem does not occur. Copy the directories apparatus, experiments, log and sas the last 2 are empty to get you going. Two optional components are available which previously were included in the base package.
Sample configuration and Spectra files are available to get you started. These datasets are large in file size, need to have variable access controls, and are essential for sharing throughout the image processing research community as a base for research and to ensure scientific reproducibility. Midas can be extended through plugin modules to allow customized data analysis, visualization, and server-side processing.
Midas is easy to use, even for non-programmers such as biological and clinical researchers, and it allows them to share data with their collaborators. The flexible group and permission system allows control over whom the data is shared with. By providing a centralized data resource, there is an authoritative version of the data that can be tracked over time for changes. The plugin system allows for significantly enhanced functionality over the stock Midas system, such as visualizing imaging results in the browser or performing server-side processing.
In this paper, we present a specific application of the plugin system, describing how it allows for creating image processing pipelines by experts familiar with these techniques and making these available to collaborators who may lack this expertise. The Rodent Imaging plugin for Midas that we describe below allows an image processing expert to construct a pipeline of executables, create a web-based user interface, manage jobs, visualize intermediate results and create compositions of multiple pipelines. Since input data and output data are centrally stored, the user experience for sharing with collaborators is tremendously improved and simplified.
Collaborators who are not image processing programmers are also given an opportunity to run complex image processing tools without having to understand command line execution or complex file operations. Furthermore, the users can follow the results of the image processing pipelines as they are brought back into Midas, preview the results in the web, and can share these results with other researchers. The computational environment and set of parameters are also shared and available to all collaborators.
As the three inputs to image processing—parameters, input data and computational environment—are shared between researchers, this system yields greater reproducibility of results, which is a core principle of scientific research. It should be noted that the design of the actual image processing pipeline itself, such as the choice of particular registration and segmentation methods to use, is beyond the scope of this current manuscript.
This image processing pipeline created with this plugin and the outputs of running the pipeline are described elsewhere Budin et al. In this paper, we focus on describing a novel plugin to the Midas platform that enables the image processing experts to port such a pipeline into Midas, creating a streamlined web-based user interface. The rest of this paper is organized as follows. In Section Materials and Methods, we describe the Midas system and the Midas plugin for developing image processing algorithms, followed by an example walk-through of preparing an ITK-based application for grid execution and generating a pipeline user interface UI for the application.
In Section Results, we present the pipelines we developed using this plugin and the results of running the pipelines to illustrate the performance of our platform. Finally, in Section Discussion, we discuss our experience with developing this pipeline along with possible improvements, and present guidelines for developers creating automated image processing pipelines. Midas is an open source software platform that provides scientific data management services, especially for large image data Jomier et al.
The Midas Platform provides a core set of functionality for organizing and managing files and folders in a hierarchical structure, and provides a framework for data access through various methods including web, filesystem and DICOM server. Any given resource in Midas, whether a folder or a file, can be viewable by anonymous users those that have not logged in , or restricted to only be viewable by users given certain permissions.
Upon creating a user account, a user of Midas is given a public and private folder, with appropriate permissions corresponding to each. The user can then add additional folders and files under those folders, and change permissions of any folders or files. In addition to user spaces, there are spaces called communities, which are shared spaces between users. The notion of a community is of a top-level point to add folders and files that are organized around a single group of users or purpose. Communities can have folders that are publicly available to anonymous users, or can be restricted to logged in users, or to only those users that are members of the community, moderators of the community, or administrators of the community.
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There is a Python client library Pydas 13 that wraps much of this API functionality; this library allows for programmatic interaction with a Midas server. Midas is architected to allow for expansion through plugins, which can be turned on or off to suit a particular setup.
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The plugins have access to all the core resources and functionality as well as the core views. In order to construct automated pipelines for image analysis, we have built a Midas plugin 14 that allows for the construction of pipelines based upon configuration. The inputs for a pipeline are a set of command line executables statically linked , a BatchMake script to coordinate inputs and outputs for the executables, and a configuration file to describe the UI displayed within Midas and parameter routing. To develop a particular pipeline, a BatchMake 15 script is developed that includes one or more command line executables and their parameters.
BatchMake is a scripting language that allows for local execution of a pipeline as well as execution under the HTCondor 16 grid computing environment. Local execution allows for debugging and rapid iteration, whereas execution under the HTCondor environment allows for arbitrarily large scalability within the limits of parallelism of the pipeline. For the sake of clarity, we illustrate these concepts using a simple executable that applies a Gaussian smoothing filter to the input image.
The rodent brain imaging pipeline that we have developed is considerably more complex, as can be expected; however, it is based on the same principles we discuss in detail and any noteworthy differences are pointed out. To demonstrate parts of the pipeline development process, we have created a simple ITK based application to perform a Gaussian smoothing on an image.
A midas plugin to enable construction of reproducible web-based image processing pipelines
In order for BatchMake to create HTCondor grid jobs for this executable, we need to define the command line parameters of the executable in a. In the case of a Slicer-compatible executable, the data for the. The Name element allows BatchMake scripts to refer to this application and the Path element indicates the full path to the executable on the executing nodes of the HTCondor grid, which may be different from the job submission or grid manager node. The Value element indicates what value to print when the parameter is invoked, beyond the parameter value itself; e.
Midas and the image processing pipeline plugin define and create configuration files that will be included by a BatchMake script bms file. This is how user selections and configuration settings specified in the web application will be delivered to the command line executables. In this example, we have created the contents of a configuration file gaussian. Note that the input and output files are created in locations that are available to both the submitting machine and the executing machine in the HTCondor grid.
Further arguments to the Python script file itself could be easily added to the pipeline BatchMake script. The pipeline BatchMake script GaussianFilter. The application gaussian is set to GaussianFilter, which was defined in GaussianFilter. The BatchMake executable runs with parameters GaussianFilter. DAGs are commonly used to describe workflow processes, with nodes of the graph representing steps in the workflow, and directed edges between nodes showing time order dependency between the steps.
Because the graph is acyclic, there is a known starting point and the workflow cannot loop, meaning it must finish after each individual step is finished. Only a single job, job0 is created in this very simple scenario; however, multiple jobs could be created by a more complex BatchMake script with the sequencing dependencies between the jobs expressed in the DAG file. For instance, if two executables need to be called in sequence, there would be a dependency between them with the job corresponding to the second executable being the child of the job corresponding to the first executable.
Executable jobs that are created in a loop by different iterations of the loop can occur in parallel and therefore have no ordering dependency between them. An example of constructing a more complex pipeline is presented in the GaussianFilter2. In this case, we have a pre-processing step for the entire dataset, followed by a processing step that would run on each input case, with a final post-processing step for the entire dataset. Since the three steps occur in sequence, there is an ordering dependency between them. For the processing step itself, since it occurs within a loop, the individual processing jobs the three separate cases can be parallelized.
Returning to the simpler GaussianFilter example, the file GaussianFilter. This file specifies the actual execution that will be run by HTCondor on whichever executing machine in the HTCondor grid will run this particular job. The POST script will be run on the submitting machine after the individual job, and will take in the return value from the executable job.
After execution of the GaussianFilter, the path to the output file will be passed to a Python script which will then upload the output file to the proper place in Midas. This way the user can view the status of the actual pipeline as it is processing on the HTCondor grid, from the web application.
The standard output and standard error logs of the executable are also uploaded to Midas to enable executable debugging via the web interface. The Python scripts for our actual pipelines make extensive use of the Pydas library, a Midas Python client library that enables calling Midas' API endpoints. Once an image processing pipeline has been developed and its input data have been added to a Midas instance, a pipeline UI within Midas is created through a configuration file. The UI generated from this configuration file presents options to the end user of the pipeline to select among inputs determined by the configuration file, and then starts a processing job.
At this time Midas will export all data necessary for the pipeline, and execute the BatchMake pipeline via HTCondor, which will then upload results to an output folder that was determined by the pipeline configuration file. An example of generating a pipeline UI follows. Beyond the example of the GaussianFilter presented above, we will demonstrate our work in generating UI components for a pipeline with examples from the Registration component of the rodent brain image processing pipeline developed in our plugin.
One of the innovative contributions of this work is bringing the concept of automated user interface components to the neuroimaging field. Our goal is to allow for ease of development of different pipelines within Midas by developers without much Midas experience. Then this specific pipeline controller class implements abstract methods from the superclass to define general parameters for the pipeline such as the title to display on the UI and the name of the BatchMake script to run. The BatchMake script implements the logic of creating jobs for the command line executables based on the passed in parameters, similar to the simple GaussianFilter example described above.
Using the Matlab MIDAS Toolbox with Octave
The first page in the UI for the Registration pipeline is the selection of input cases. The values set in the getCasesSelection method determine the default folder to choose cases from, but a different folder can be selected with the Browse button. The cases that are presented as checkboxes for the user to select from come from the Midas folder named 1-Converted, a subfolder of the cases folder. The Inputs parameter allows the user to select a filename pattern from a dropdown for the actual input to this pipeline, and the additional dropdowns are populated to allow the user to select additional files coming from each of the selected case directories, which gives the ability to send multiple input files to be processed together as part of the same case.
This allows the user to select a single Midas item file to be used in processing all cases. The final UI page of the Registration pipeline allows the user to select parameters, and will supply default values based on what is implemented in the getParameters method. The getOutputFolderStem method will determine where the outputs of the Registration pipeline will be uploaded back into Midas when processing is complete.
Once the developers have created BatchMake scripts for the pipeline and the Pipeline UI controller, the pipeline is available for an end user. Based on the configuration file for the pipeline, the user will be presented in step 2 with some options for setting parameters and selecting input data that is stored in Midas, or can simply choose default values if they have been set for the pipeline, and can then request execution.
In step 3 , the Midas system will create a temporary workspace for this pipeline execution on the associated filesystem, and the data referenced by this execution of the pipeline is symlinked into this temporary directory. The parameter values for this run of the pipeline are exported into this work directory for inclusion by the BatchMake script.
There will be one HTCondor job description file for each instantiation of a command line executable within the BatchMake script, and the DAG specifies the dependencies between the jobs. In step 4 , the HTCondor DAG is submitted to the HTCondor grid, which allows for the execution of the pipeline to be scaled up to the limits of available hardware or budget.
As each step of the pipeline finishes execution, a post-processing script can optionally be run, e. For the pipelines we implemented, these post-processing steps are used to load intermediate files, result files, and metadata produced by the pipeline back into Midas. Midas has a plugin to visualize any image format that ITK can read, and as a result, end users can see the intermediate and final results of their pipelines displayed within Midas itself as the results come back. Once a pipeline has been developed, an end user can run them even if that user has no image processing experience, and can run them in a completely reproducible manner.
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After creation of pipelines, pipeline composition becomes possible, so that a user can run multiple pipelines in sequence on a given dataset, changing parameters in order to visualize how this will affect the results. The user may also run pipelines using any of several outputs from a previous step, enabling them to see how changes in one stage of the pipeline affect a later stage before accepting a single output of a stage as the canonical input for the following stage or the final result, once that output has passed manual quality assurance tests.
The Midas Rodent plugin was developed to enable creation of image analysis pipelines, and was used specifically to construct a composite pipeline that perform region-based analysis of 3D rodent brain MRI images. This pipeline and the results of running it on our input datasets are described in detail in Budin et al. The pipeline includes both pre-existing tools and tools developed for this pipeline.
The pipeline comprises six major steps: The output of each step is used as the input to the subsequent step. The inputs to the overall pipeline are images acquired by MRI, and then stored in Midas. Each of the pipelines was built and debugged separately, and is designed to be run independently of the others.
Once a given pipeline is debugged and validated, we can use them to process our subject cases. The output of a pipeline step, once deemed acceptable, becomes the input to the succeeding step. We may sometimes need to reprocess a given pipeline many times to achieve the correct parameter set, or to compare the outputs generated with different parameters or even alternative pipelines. This can be viewed as a data quality assurance process. After multiple runs of a given pipeline, the outputs of each of those runs—which would vary based on parameter selection—can be examined for quality and potential errors.
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Once the output of a certain run is approved, it can be used as the canonical input to the following step. Since the parameters of each pipeline run are tracked, it is straightforward to find the parameters that went into creating any particular output run of a pipeline, and hence that same pipeline run is reproducible. This illustrates how using our system seamlessly resolves the data provenance problem that often causes issues in large or even middle-scale studies.
Due to running the pipelines on the grid system, we were able to execute the cases in parallel, which saved significant wall-clock running time. This also helped to develop and debug the pipelines faster, as we could see the results from a pipeline run much more quickly than if we had to run each step in series. However, there was a cost in development time, as the additional layers of preparing the executables for grid execution incurred overhead compared to simply running the executables locally. Debugging in the HTCondor environment is not as straightforward as debugging an executable on its own.
There could be bugs in the executables, bmm file, the logic of the BatchMake script, the networked file system or HTCondor grid setup, and the multi-layered interaction between these components added complexity. Once a pipeline is developed, it enables much easier development of subsequent pipelines, as inputs could be quickly rerun to evaluate parameter choices and their effects on the later pipelines.
The centralized nature of this platform was helpful in development from the standpoint of data sharing, since multiple developers were working together on different pipelines and aspects of the overall system.