This project aims at developing software technology capable of automating bone histomorphometry, which at the moment is mostly done manually through cumbersome and laborious interactive processes. Our plan is to use state-of-the-art image processing techniques to automate detection, measurement and quantification of biological effects directly over the stained images acquired from microscopic devices. We plan to employ a three-step approach for the automated quantification: (i) refine our sample processing and imaging steps and validate the computer derived analytical results with the traditional manual approach; (ii) using a larger set of in house mice with different genetic backgrounds, develop automated histomorphometric analytical pipelines and a database to house the raw data and the analysis outcomes in a format that would be attractive to investigators; and (iii) beta test this strategy with four investigators at outside institutions who submit samples to the analytical pipeline for processing and analysis. The third step will utilize the company's existing visual dataflow framework and build a business model which offers not only the delivery of the final image analysis outcome, but also the entire analysis process itself to ensure analysis provenance to the scientists. The potential impact of our proposed R&D work is enormous. Currently the manual processing of in-house histomorphometry requires lengthy man hours, is prone to error and is becoming too costly to run. In addition, due to in-house oriented individualized operations, comparing the analysis outcome against community-wide gold standards has been very difficult. Our development can create a centralized, cost effective service enterprise with which scientists can rapidly acquire measurement data at cost in a format that can easily be re-examined and validated. Furthermore, the centralized service can store a large collection of published "reference" images and measurement values so that scientists can more efficiently compare phonotypical changes against the references. When this centralized contractual based image processing is established and widely-used, the cost of histomorphometry could become significantly lower, while the quality of science dramatically increases.
Public Health Relevance: Perceived societal, educational, and scientific
Benefits: Our proposed software can potentially revolutionize bone histomorphometry. Scientists will be able to do image analysis in a much shorter time with an improvement in quantification quality. In addition, using the perceived service model scientists will be able to utilize histomorphometry at far greater scale than what is current available due to the significant reduction in cost and speed. As for the specific impact on the science, once established our proposed automated method could serve as a high throughput mechanism for evaluating all the genetically modified mice for evidence of altered bone structure and observing the change in bone architecture.
Public Health Relevance: Perceived societal, educational, and scientific
Benefits: Our proposed software can potentially revolutionize bone histomorphometry. Scientists will be able to do image analysis in a much shorter time with an improvement in quantification quality. In addition, using the perceived service model scientists will be able to utilize histomorphometry at far greater scale than what is current available due to the significant reduction in cost and speed. As for the specific impact on the science, once established our proposed automated method could serve as a high throughput mechanism for evaluating all the genetically modified mice for evidence of altered bone structure and observing the change in bone architecture.
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