SOURCES SOUGHT
R -- R&D in Computer-Assisted Processing of Digitized Histology Images
- Notice Date
- 8/20/2012
- Notice Type
- Sources Sought
- NAICS
- 611310
— Colleges, Universities, and Professional Schools
- Contracting Office
- Department of Health and Human Services, National Institutes of Health, National Library of Medicine, 6707 Democracy Blvd., Suite 105, Bethesda, Maryland, 20894, United States
- ZIP Code
- 20894
- Solicitation Number
- NIHLM2012495
- Archive Date
- 9/11/2012
- Point of Contact
- Keturah D. Busey, Phone: 3014966546, Robin Hope, Phone: 301-496-6546
- E-Mail Address
-
buseyk@mail.nlm.nih.gov, Robin.Hope@nih.gov
(buseyk@mail.nlm.nih.gov, Robin.Hope@nih.gov)
- Small Business Set-Aside
- N/A
- Description
- This Small Business Sources Sought Notice is for informational and planning purposes only and shall not be construed as a solicitation or as an obligation or commitment by the Government. This notice is intended strictly for market research. The National Institutes of Health (NIH), National Library of Medicine (NLM) is conducting a market survey to help determine the availability and technical capability of qualified small businesses, veteran-owned small businesses and/or HUBZone small businesses capable of serving the needs identified below. This is a Small Business Sources Sought Notice. This is NOT a solicitation for proposals, proposal abstracts, or quotations. The purpose of this notice is to obtain information regarding: (1) the availability and capability of qualified small business sources; (2) whether they are small businesses; HUBZone small businesses; service- disabled, veteran-owned small businesses; 8(a) small businesses; veteran-owned small businesses; woman-owned small businesses; or small disadvantaged businesses; and (3) their size classification relative to the North American Industry Classification System (NAICS) code for the proposed acquisition. Your responses to the information requested will assist the Government in determining the appropriate acquisition method, including whether a set-aside is possible. An organization that is not considered a small business under the applicable NAICS code should not submit a response to this notice. Background The proliferation of medical information in the form of digital images has created an opportunity and a challenge for technology to play a role in the analysis and understanding of this data for both research and clinical purposes. In the field of pathology, efforts are underway to use technology to digitally acquire, manage, and analyze histology images with the goals of gaining efficiency and accuracy in interpreting the image data for assessing disease at the tissue level in support of treatment and management programs for health care improvement. The increasing amounts of image data acquired in clinical care centers have created a burden for interpretation by human experts that may reach unsustainable levels. Computer-assisted methods may be able to play a significant role in off-loading demands on experts. These methods must address (1) the acquisition, storage, retrieval, and display of the histology image data, (2) the analysis and interpretation of the histology image data, and (3) the integration of the computer-assisted methods into the clinician (or biomedical researcher) workflow. Proposed Work The primary focus of the proposed work is described in the Background steps (A) characterize the epithelium geometry, (B) segment, measure and characterize epithelium contents, and (C) classify sub-regions in the epithelium in disease categories of Normal, CIN1, CIN2, and CIN3. For (A), research currently sponsored by NLM, a hybrid distance transforms and end-segment adaptive algorithm has been investigated for medial axis determination. The investigated technique has been successful in estimating the medial axis for epithelium regions where the epithelium region is somewhat rectangular in structure. For more rounded epithelium regions, the current algorithm is not able to detect the medial axis correctly. This is primarily due to the fact that the algorithm currently only uses geometrical information to compute the medial axis. As a possible method of overcoming this difficulty, the contractor will investigate the integration of epithelium region shape, cell density and texture features to estimate the orientation and direction of the epithelium region as a preprocessing step to guide the distance transform method for initial medial axis determination for the hybrid algorithm. This would combine both geometrical (structural) and texture-based information for computing the medial axis. For (B), the contractor will extend research currently sponsored by NLM to segment and extract mathematical features from contents of epithelium; this will include the quantitative characterization of biological features such as number of nuclei per unit area, nuclei optical density, nuclei/cytoplasm ratio, and nuclei shape characteristics. For (C), in the research currently sponsored by NLM, the classification of sub-regions within the epithelium is begin done by use of digitized uterine cervix images supplied by NLM, where expert truth classifications for epithelium regions are available, but where the only sub-region truth set is created by a research engineer. The contractor will seek an expert medical consultant at the Phelps County Regional Medical Center (Rolla, MO), or other medical centers of opportunity, for guidance in establishing truth classifications for these sub-regions, and for extending the current ground truth data set. In addition, for (C), the contractor will investigate state-of-the-art classification techniques for disease classification in these images and will implement and test the performance of these techniques. The classification work will include (a) classification of sub-regions within epithelium segments and (b) methods to intelligently combine sub-region classifications into classifications of the image data at larger spatial levels, such as entire epithelium segments, and the image as a whole. The contractor will investigate and take advantage of classification techniques that have been reported as effective in the technical literature, including specifically techniques used in the field of skin lesion discrimination. The work proposed here is largely concerned with (2) above, the analysis and understanding of the histology image data. An immediate challenge in this work is the large size of histology images. A typical digitized histology image of the uterine cervix spans tens of thousands of pixels in both width and height, and has three color planes. The contents of the images may very roughly be described as epithelium, stroma, and background. The relative proportions of each of these categories is highly variable from image to image, and the epithelium and stroma categories show a high degree of geometrical configurations and commonly contain complex or diffuse structures including glands, red blood cells, squamous cells, columnar cells, cytoplasm, and cell nuclei. The proposed work focuses on disease in the epithelial tissue of the uterine cervix, so the region of interest is the epithelial region. The first challenge, then, is the development of methods to reliably locate this region within the image. NLM has initiated work toward this goal, which is ongoing, and it is envisioned that the proposed analysis work will take advantage of the available methods developed by NLM for location of epithelial tissue and/or take advantage of sets of epithelial regions of interest which have been acquired by NLM by manual or other methods. After the epithelial regions has been located in the image, the proposed analysis work includes (A) characterizing the geometry and orientation of the epithelium, (B) implementing methods to segment, characterize, and/or measure structures within the epithelium that are believed to be of relevance for disease classification, and (C) apply classification algorithms to the data acquired in step (B) to classify subregions within the epithelium into disease categories. The overall goal of the government is to incorporate this work into an integrated system for the analysis and understanding of whole-slide histology images for improvements in clinical care and biological research. Toward this goal, the contractor will • take advantage of code previously developed by the government for the location of epithelium regions within the whole slide images by using this code, where practical, for the location of epithelium regions for analysis and disease classification; • make recommendations for modifications and improvements to this code, in consultation with the government; • implement modifications and improvements to this code, in consultation with the government. Required Tasks The following tasks are to be completed. Full detail is given in the Proposed Work section of this Statement of Work. All image analysis and annotation software delivered shall be written in MATLAB or as negotiated with the government. Task description: Task 1: Develop initial algorithms for disease classification of digitized uterine cervix histology images The investigator will conduct R&D for computer-assisted classification of digitized uterine cervix histology images, will create an initial version of classification algorithms, and produce preliminary test results, using a government-provided set of images. Task 2: Extend and evaluate algorithms for disease classification of digitized uterine cervix histology images The investigator will conduct R&D for computer-assisted classification of digitized uterine cervix histology images, and will extend and comprehensively evaluate the algorithms developed in Task 1. The contractor will also provide test results on any additional uterine cervix images that have been acquired, or that were provided by the government during the first half of the contract performance period. The National Library of Medicine seeks organizations with demonstrable capability, knowledge of, and experience in uterine cervical cancer histology image classification for the purpose of improving the state of art in clinical decision support. The demonstrable experience must include the following: (i) development of image processing and machine learning software algorithms for color, texture, geometry, and object identification within histological image regions, (ii) identifying biologically relevant regions including stroma and epithelium, and (iii) prior experience in creating a computer assisted system for diagnosis of digitized histology images of the uterine cervix into standard diagnostic categories prevailing in the oncologic gynecology community. Anticipated Period of Performance It is anticipated that the period of performance shall be for twelve (12) months from the date of award. Awards are anticipated to be made in September of 2012. Other Important Considerations The proposed acquisition will be procured in accordance with the policies and procedures under FAR 13-Simplified Acquisition Procedures. All responsible sources may submit a capability statement which will be considered by the National Library of Medicine. This Sources Sought Notice is not a Request for Proposal (RFP), nor is an RFP available. Interested firms responding to this Sources Sought Notice must adhere to the following: (a) Provide a capability statement demonstrating relevant experience, skills and ability to fulfill the Government's requirements for the above. The capability statement should contain enough sufficient detail for the Government to make an informed decision regarding your capabilities; however, the statement should not exceed 10 pages. (b) The capability statement must identify the responder's: small business type and size; DUNS number; NAICS code; and technical and administrative points of contact, including names, titles, addresses, telephone and fax numbers, and e-mail addresses. (c) All capability statements must be submitted electronically no later than 12:00pm eastern standard time on Friday, August 31, 2012 to Keturah Busey, at buseyk@mail.nlm.nih.gov. Disclaimer and Important Notes: This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization's qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, a pre-solicitation synopsis and solicitation may be published in Federal Business Opportunities. However, responses to this notice will not be considered adequate responses to a solicitation. Confidentiality: No proprietary, classified, confidential, or sensitive information should be included in your response. The Government reserves the right to use any non-proprietary technical information in any resultant solicitation(s).
- Web Link
-
FBO.gov Permalink
(https://www.fbo.gov/spg/HHS/NIH/OAM/NIHLM2012495/listing.html)
- Place of Performance
- Address: TBD, United States
- Record
- SN02846553-W 20120822/120820235433-4fce6745d5ff0d96ac0822d58ef37aeb (fbodaily.com)
- Source
-
FedBizOpps Link to This Notice
(may not be valid after Archive Date)
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