Can “love” be imaged and analyzed?
No one at ImageIQ has a Ph.D. in love. We help researchers improve their preclinical research and clinical trial data with our imaging expertise, cutting-edge technology, and unique approach to image analysis. So, for this Valentine’s Day we’ve decide to spread the love by analyzing what very well may be the very anatomical basis for love: the cardiac muscle fiber. We accomplished this by using our new, paradigm-shifting technology platform for online preclinical quantitative image analysis: ImageQuantify.com.
For this challenge, we leveraged our Heart Muscle Fiber Analysis IQbot, which makes your analysis easier, faster, and consistently accurate — give it a try by creating an account and putting your free credits to work. You can even share your images and data online with whomever you like. Or love. After all, it is Valentine’s Day.
|Total Fibrils||Mean Area (pixels)||Mean Aspect Ratio||Mean Roundness||Mean Diameter (pixels)||Mean Perimeter (pixels)|
Your Valentine’s Day gift: A 15-minute webinar on Heart and Skeletal Muscle Analysis.
HOW DID WE DO IT?
We set out to quickly and accurately analyze cardiac muscle fiber histological cross sections, extracting the number of fibrils as well as each fibril’s average and individual area, aspect ratio, roundness, perimeter length, and diameter. The quantification of muscle fiber size and shape properties provides researchers valuable insight into many metabolic and cardiovascular disorders. Our unique IQbot filters an image and performs morphological operations to create masks of the image background, connective tissue, and an applied reticulin stain. The IQbot then segments the reticulin for fiber identification and excludes very large objects that represent regions of longitudinally sectioned muscle fibers. Finally, connected fibers are separated. In addition to producing the morphological data, the IQbot provides a pseudo-colored output image overlay, in which the segmented fibers are colored red and superimposed upon the original input image. After all, when it comes to quantitative image analysis data: seeing is believing.