 |
Page History
...
Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. In addition, each pathology-imaging vendor produces its own image management systems, making image analysis systems proprietary and not standardized. The result is that images produced on different systems cannot be analyzed via the same mechanisms. Not only does this lack of standards and the dominance of proprietary formats impact digital pathology, but it prevents digital pathology data from integrating with radiographic, genomic, and proteomic data.
This component of this sub-project will incorporate the The purpose of the digital pathology component of CTIIP is to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA. The team is using OpenSlide, a vendor-neutral C library OpenSlide with caMicroscope to directly serve whole slide pathology images from the majority of digital pathology vendors. This will be accomplished , to extend the software of caMicroscope, a digital pathology server, to provide the infrastructure for these data mashups. 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. A single digital pathology server would allow NCI to With the addition of support for basic image analysis algorithms, (CKK: what?). These additional features of caMicroscope will make it possible to include digital pathology images within TCIA / and NBIA and provide a logical bridge from proprietary pathology formats to DICOM standards. Specifically, the team will expand the functionality of the caMicroscope digital pathology platform to include support for some of the common formats adopted by whole slide vendors. The OpenSlide library would make this functionality possible.
•Incorporate support for basic image analysis algorithms into caMicroscope.
...
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 as well. It will make it possible to create integrative queries using data from TCIA and TCGA. 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.
Image annotations also require standards so that they can be read across domains along with the rest of the image data. caMicroscope will also be extended to include standards-based image annotation using the Annotation Image Markup (AIM) standard.
Data federation, a process whereby data is collected from different databases without ever copying or transferring the original data, is part of the solution as well. It requires a shared semantic scheme and a supporting software framework to link databases. Most importantly for the success of CTIIP, data federation will make it possible to create integrative queries using data from TCIA and TCGA. 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.
Integrating Imaging and Molecular Data
This infrastructure can be expanded to include more data types and additional integration, which will provide analytic and decision support to researchers, who can then pursue a broader set of novel community research projects.
Integrating Imaging and Molecular Data
All three research domains will clearly need an imaging archive that can be leveraged for integration across multiple data types and sources. For example, TCGA program has the goal of producing a comprehensive genomic characterization and analysis of 200 types of cancer and providing this information to the research community. TCIA and the underlying National Biomedical Image Archive (NBIA) software stack were created to manage well-curated, publicly-available collections of medical image data, including diagnostic images associated with the tissue samples sequenced by TCGA. TCIA currently supports All three research domains will clearly need an imaging archive that can be leveraged for integration across multiple data types and sources. For example, TCGA program has the goal of producing a comprehensive genomic characterization and analysis of 200 types of cancer and providing this information to the research community. TCIA and the underlying National Biomedical Image Archive (NBIA) software stack were created to manage well-curated, publicly-available collections of medical image data, including diagnostic images associated with the tissue samples sequenced by TCGA. TCIA currently supports over 40 active research groups including researchers who are exploiting the existing linkages between TCGA and TCIA. TCIA has recently released an Application Program Interface (API) that provides a REST API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas,and this API is being designed to support federation of multiple information repositories using the concepts of a data mashups. This infrastructure can be expanded to include more data types and additional integration, and provide analytic and decision support, which will act as a foundation for a broader set of novel community research projects.
Project 1: Integrated Query System for Existing TCGA Data
1) AIM 1 - Integrated query system for existing TCGA data (including improved pathology systems)
a) Histopathology
i) Incorporate Openslide with caMicrosocope enabling caMicrosocope to directly serve whole slide pathology images from the majority of digital pathology vendors.
ii) Incorporate support for basic image analysis algorithms into caMicroscope.
iii) Standards-based image annotation utilizing the Annotation Image Markup (AIM) standard.
b) Integrative Queries
i) Programmatic Access to Data to TCGA-related image data.
ii) Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Histopathology
•
Integrative Queries
•Programmatic Access to Data to TCGA-related image data.
•Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Integrative Query System
Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Integrative Queries
Programmatic Access to Data to TCGA-related image data.
Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
What the data is used for
Relate data from TCIA, caMicroscope, animal model
genomics, animal
how do we make a decision on a firm diagnosis?
Get queries and relate it to the human data and vice versa
System should integrate clinical data (from TCGA), preclinical data (comes from UC Davis)
Use case: Breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?
If you treat a mouse model that has an ER negative status with a certain drug, what is the outcome? Then see this in humans.
We are setting up the data structure so when that is done, we'll be able to see what use cases are possible.
To make data comparable, we must collect it in a structured fashion. Common Data Elements for TCGA.
We are pulling data out of caDSR (ER negative and positive, other common data elements) and we are asking Bob Cardiff's team to ask the same questions so that we can compare human and mouse data.
We are exploring the standardization of informatics. Use all the tools we have to create standard informatics to compare patient to animal data. We are using the available standards: DICOM, AIM, micro AIM. Fundamental to integrative queries.
If you did an integrative query, how would you do it? Data calls to do different integrative queries. How would you use sufficient standard data. Come out with information that will allow you to make a decision. Pilot challenges to compare the decision support systems for three domains.
We need a clear explanation of how to do this.
Data mashups that allow us to
Explain our complicated project in a simple manner so they understand why we are doing and what we are doing.
Pathology problems:1. proprietary data formats that cannot be displayed and manipulated in the same tools. Solution is to integrate caMicroscope with OpenSlide (allows us to read prop. formats without converting images). Makes a large number of image formats accessible. 2. no standard for markups and annotations. So we're creating microAIM.
Challenges
Solutions
The first step towards the goal of image data integration is the creation of an image server that can host and serve digital pathology images for any of the major vendors without recoding, which often introduces additional compression artifacts. This image server will be caMicroscope, with its functionality expanded by the OpenSlide library.
- caMicroscope is a digital pathology viewer provides researchers with an HTML5-based web client that can be used to view a digitized pathology image at full resolution. While it is standards-based, implementing both the Annotation and Image Markup (AIM) and Digital Imaging and Communications in Medicine (DICOM) standards, it supports limited formats adopted by whole-slide vendors.
- OpenSlide is a C library that can read whole-slide images in many common formats adopted by whole-slide vendors.
This project is also proposing a standard for markup and annotations called microAIM.
What the data is used for
Relate data from TCIA, caMicroscope, animal model
genomics, animal
how do we make a decision on a firm diagnosis?
Get queries and relate it to the human data and vice versa
System should integrate clinical data (from TCGA), preclinical data (comes from UC Davis)
Use case: Breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?
If you treat a mouse model that has an ER negative status with a certain drug, what is the outcome? Then see this in humans.
We are setting up the data structure so when that is done, we'll be able to see what use cases are possible.
To make data comparable, we must collect it in a structured fashion. Common Data Elements for TCGA.
We are pulling data out of caDSR (ER negative and positive, other common data elements) and we are asking Bob Cardiff's team to ask the same questions so that we can compare human and mouse data.
We are exploring the standardization of informatics. Use all the tools we have to create standard informatics to compare patient to animal data. We are using the available standards: DICOM, AIM, micro AIM. Fundamental to integrative queries.
If you did an integrative query, how would you do it? Data calls to do different integrative queries. How would you use sufficient standard data. Come out with information that will allow you to make a decision. Pilot challenges to compare the decision support systems for three domains.
We need a clear explanation of how to do this.
Explain our complicated project in a simple manner so they understand why we are doing and what we are doing.
Pathology problems:1. proprietary data formats that cannot be displayed and manipulated in the same tools. Solution is to integrate caMicroscope with OpenSlide (allows us to read prop. formats without converting images). Makes a large number of image formats accessible. 2. no standard for markups and annotations. So we're creating microAIMThis infrastructure can be expanded to include more data types and additional integration, which will provide analytic and decision support to researchers, who can then pursue a broader set of novel community research projects.
Small Animal/Co-clinical Improved DICOM Compliance and Data Integration
...
Once participants upload their results, they can see them in ePad.
Challenges
...
Scenarios
Need to generate proper therapy for a patient. Look at in vivo imaging, radiology and pathology, run a gene panel to look for abnormal. Look at co-clinical trials (model of a tumor in a mouse that is similar to a human. Experiment therapies on mice.) Run an integrative query to develop a sophisticated diagnosis. Search big data.
...
