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Introduction to CTIIP
Most cancer diagnoses are made based on images. You have to see a tumor, or compare images of it over time, to determine its level of threat. Ultrasounds, MRIs, and X-rays are all common types of images that radiologists use to collect information about a patient and perhaps cause a doctor to recommend a biopsy. Once that section of the tumor is under the microscope, pathologists learn more about it. Radiologists and pathologists represent different scientific disciplines. To gather even more information, a doctor may order a genetic panel. If that panel shows that the patient has a genetic anomaly, the doctor or a geneticist may search for clinical trials that match it, or turn to therapies that researchers have already proven effective for this combination of tumor and genetic anomaly through recent advances in precision medicine.
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| Sub-Project Name | Description |
|---|---|
| Digital Pathology | Addresses the accessibility of digital pathology data through the integration of OpenSlide, improves tools for annotation and markup of pathology images through the development of microAIM (μ-AIM), and integrates analysis tools with caMicroscope. These developments increase the interoperability in each of the targeted research domains: clinical imaging, pre-clinical imaging, and digital pathology imaging. |
| Integrated Query System | Provides a data mashup interface that accesses TCGA clinical and molecular data, TCIA in-vivo imaging data, caMicroscope pathology data, relevant imaging annotation and markup data, and a pilot data set of animal model data. |
| DICOM Standards for Small Animal Imaging; Use of Informatics for Co-clinical Trials | Addresses the need for standards in pre-clinical imaging and applies the informatics tools created by the Digital Pathology and Integrated Query System sub-projects to co-clinical trials. |
| Pilot Challenges | Pilot challenges are a tool to find suitable image analysis algorithms. The pilot challenges would use limited data sets for proof-of-concept, and test the informatics infrastructure needed for more rigorous “Grand Challenges” that could later be scaled up and supported by extramural initiatives. |
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Within the three research domains that CTIIP intends to make available for integrative queries, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. Those standards include Annotation and Image Markup (AIM), which allows researchers to standardize annotations and markup for radiology images, and Digital Imaging and Communications in Medicine (DICOM), which is a standard for handling, storing, printing, and transmitting information in medical imaging. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems. µAIM is currently The micro-Annotation and Image Markup (µAIM) model is currently in development to serve the unique needs of the pre-clinical domain.
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| Domain | Data Set | Applicable Standard | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Molecular and Clinical ImagingData | The Cancer Genome Atlas (TCGA) molecular and clinical and molecular data | N/A | ||||||||
| Clinical Imaging | The Cancer Imaging Archive (TCIA) in vivo imaging data | DICOM | ||||||||
| Pre-Clinical | Small animal models | Supplement 187: Preclinical Small Animal Imaging Acquisition Context
N/A A standard exists but has not yet been adopted. (ask Ulli) | ||||||||
| Digital Pathology | caMicroscope | DICOM is applicable but has not yet been adopted(ask Ulli). | ||||||||
| All | Annotations and markup on images | µAIM is in development. |
Digital Pathology and Integrated Query System
One of the goals of this sub-project is to create a digital pathology image server that can accept whole slide images from multiple vendors and display them despite the proprietary formats they were created in. They This is accomplished by integrating the OpenSlide
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The team is using OpenSlide, a vendor-neutral C library, to extend the software of caMicroscope, a digital pathology server. The extended software will support some of the common formats adopted by whole slide vendors as well as basic image analysis algorithms. With the incorporation of common whole slide formats, caMicroscope will be able to read whole slides without recoding, which often introduces additional compression artifacts, and provide a logical bridge from proprietary pathology formats to DICOM standards.
Image markups and annotations also require standards so that they can be read by different imaging disciplines along with the rest of the image data. caMicroscope will also be extended to Support for the μ-AIM model will be added to caMicroscope so that researchers can include image annotation and markup features using the micro-Annotation and Image Markup (μ-AIM).in digital pathology data.
caMicroscope Slide with Markup
With caMicroscope'With caMicroscope's support for basic image analysis algorithms, researchers can use this tool to enable analytic and decision support using digital pathology images.
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Data federation, a process whereby data is collected from different databases without ever copying or transferring the original data, is part of the new infrastructure. The software used to accomplish this data federation is Bindaas. Bindaas is middleware that is also used to build the backend infrastructure of caMicroscope. The team is extending Bindaas with a data federation capability that makes it possible to query data from TCIA and TCGA.
The Integrated Query System will access multiple data types in a federated fashion, meaning that the original data will reside in independent systems. The Integrated Query System will provide an interface scientists can use to select the data types they want to combine, or "mash up," based on their own research questions.
The following table presents the data types and their sources that the Integrated Query System will make available.
a middleware used to develop web services that allow data providers to share data, stored in databases, using a popular standard for developing web services called Representational State Transfer. Developers can use the REST interface with most modern languages to rapidly create and deploy applications that can consume data contained in the underlying database. Bindaas enables resource providers to rapidly generate APIs with only an understanding of the underlying data model. It is able to do so because it uses a declarative programming model that allows data providers to create REST APIs without having to write a single line of code.
The Integrated Query System will access multiple data types in a federated fashion, meaning that the original data will reside in independent systems. The Integrated Query System will provide an interface scientists can use to select the data types they want to combine, or "mash up," based on their own research questions.
The following table presents the data types and their sources that the Integrated Query System will make available.
| Data Types in the Integrated Query System | Data Source |
|---|---|
| Genomic | Google Genomics Cloud |
| Clinical | Downloaded from TCGA and stored in a customized database at Emory University |
| Preclinical | Customized database at Emory University |
| Radiology | |
| Data Types in the Integrated Query System | Data Source |
| Genomic | Google Genomics Cloud |
| Clinical | Downloaded from TCGA and stored in a customized database at Emory University |
| Preclinical | Customized database at Emory University |
| Radiology Images (Human and Animal) | TCIA |
| Radiology Image Annotation and Markup | AIM Data Service (AIME) |
| Pathology Images (Human and Animal) | caMicroscope |
| Pathology Image Annotation and Markup | uAIM Data Service (uAIME) |
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Challenges are often conducted in conjunction with scientific conferences. The following Pilot Challenges, supported by the CTIIP project and described in the following table, were part of the Computational Brain Tumor Cluster of Event (CBTC) 2015 which was held on October 9, 2015 in Munich, Germany, in conjunction with MICCAI 2015.
| MICCAI 2015 Challenges | Sample Image | Description | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Combined Radiology and Pathology Classification
|  | The datasets for this challenge are Radiology and Pathology images obtained from the same patients. Each case corresponds to a single patient. There is one Radiology image and one whole slide tissue image for each case. The training set contains a total of 32 cases: 16 cases that are classified by pathologists as Oligodendroglioma and 16 cases classified as Astrocytoma. The test set will have 20 cases. Please note that the number of cases in the test set may not be equally partitioned between the two sub-types. The whole slide tissue images are stored in Aperio SVS format. There are open source tools and libraries that can read these images: OpenSlide
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Segmentation of Nuclei in Pathology Images
|  | Segmentation of Nuclei in Pathology Images | The characteristics of cancer nuclei are central components in many aspects of pathology classification. Nuclear features, when combined with “omics,” have been shown by many research groups to be linked to patient outcome. Although there are many important and predictive features, characteristics of cancer nuclei are the most important. Virtually without exception, all commercial systems and academic groups in the digital Pathology area make use of nuclear segmentation algorithms. The ability to segment and then classify nuclei is therefore a key task. | The goal of this challenge is to evaluate the performance of algorithms for detection and segmentation of nuclear material in a tissue image. Participants are asked to detect and segment all the nuclei in a given set of image tiles extracted from whole slide tissue images. The algorithm results will be compared with consensus pathologist-segmented sub-regions. Winners will be ranked based on their nuclei segmentation best matching the reference standards. The reference standard for the challenge will be pathologist-generated nuclear segmentation on select regions of TCGA Glioma whole slide images for the challenge. |
A team from Massachusetts General Hospital will guide the Pilot Challenges. They are using Medical Imaging Challenge Infrastructure (MedICI), a medical imaging challenge platform, to support the challenges. MedICI, in turn, uses the CodaLab framework, an open-source challenge platform developed by Microsoft Research and others in the medical imaging and machine learning communities. Because CodaLab does not have built-in imaging handling, display or annotation capabilities, the team is building on two application packages, ePad and caMicroscope, to provide those features. For example, once participants upload their results, they can see them in ePad.
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