It is well established that aging and many chronic diseases, such as cancer and heart failure, are associated with significant losses in skeletal muscle mass and strength in humans. There is agreement across the muscle biology community that important morphological characteristics of muscle fibers, such as fiber area, the number and position of myonuclei, cellular infiltration and fibrosis are critical factors that determine the health and function of the muscle. However, at this time, quantification of muscle characteristics from standard histological and immunohistological techniques is still a manual or, at best, a semi-automatic process. This process is labor intensive and can be prone to errors, leading to high inter-observer variability. On the other hand, when muscle characteristics are calculated by computer-aided image analysis, data acquisition times decrease and objectivity improves significantly. The objective of this Phase I STTR project is to build a fully automatic, intelligent, and high throughput image acquisition and analysis software for quantitative muscle morphological analysis on digitized muscle cross-sections. We propose to utilize the most recent technical advances in machine learning and biomedical image analysis. This includes a newly developed deformable model and mean-shift based seed detection algorithm for better segmentation accuracy;an asymmetric online boosting based machine learning algorithm which allows the software to learn from errors and adjust its segmentation strategies adaptively;and a data parallelization schema using the graphic processing unit (GPU) to handle the computational bottleneck for extremely large scale image, such as whole slide scanned specimens. We believe that this software, equipped with the most advanced technical innovations, will be commercially attractive for the skeletal muscle research community including basic scientists, clinician scientists, and the pharmaceutical industry.
The specific aim are: 1) Develop, implement, and validate an automatic biological image analysis software package for skeletal muscle tissue;2) Develop a novel online updated intelligent artificial intelligence unit to enable the software to learn from errors;3) Build a novel high performance computing unit to enable fast and high throughput automatic image analysis, which is capable of processing whole slide scanned muscle specimens. The analysis approach proposed will provide more consistent, accurate, and objective quantification of skeletal muscle morphological properties and the time for data analysis will be reduced by over a factor of 100 for standalone version and 2000 for parallel version. The long-term goal of Cytoinformatics, LLC for the Phase II stage is to apply the software to analyze histology/pathology from human muscle biopsy samples and extension of the software to other biological tissues, such as adipose tissue.
Important features of muscle fibers, such as fiber area, the number and position of myonuclei, cellular infiltration and fibrosis are critical factors that determine the health of the muscle. However quantification of muscle features from digitized images is still a manual or, at best, a semi-automatic process. The objective of this Phase I STTR project is to build software using the most recent technical advances in machine learning and biomedical image analyses to significantly move the skeletal muscle basic and clinical research fields ahead.