Introduction Chemical tissue staining is currently used for studying cancer pathology: tissue cores extracted from a patient are thinly sliced and stained using chemical dyes to highlight different cell types. The stained tissue samples are examined under a microscope to diagnose disease.

The Bioimaging Science and Technology group at the Beckman Institute uses infrared spectroscopy to directly collect chemical information. The goal is to use this quantitative information to improve disease diagnosis by providing more accurate information to pathologists.

Methods and results

The first step in our program is loading the ENVI header information. This includes all of the properties of the image, such as the x and y resolution, the resolution of the infrared spectra (number of bands), and the units of measurement used. These parameters define how the random forest is constructed. Our program stores them as variables for later use.

Due to the large file size of the ENVI images, an entire image cannot be stored in memory, and must rather be streamed from the hard drive and classified sequentially. This is known as out-of-core processing. Our program feeds individual chunks into the classifier one by one, building up a fully classified image.

Because four collaborators all worked on the same program concurrently, utilizing a distributed version control system called Git was extremely important. This allowed us to keep track of and comment on any edits made in our code. Git also allows for branching, so separate users can work independently, then merge all the branches back into one main master branch.

Our code was managed using Cmake, which allows us to easily link our programs with external libraries, such as Qt and ALGLIB, which were used for user-interface design and classification.

Spectroscopic Images

This resulting infrared signal produces a digital image, in which each pixel corresponds to a frequency in a spectrum graph where the point will vibrate. This data is stored using the ENVI (Environment for Visualizing Images) format. This large, specialized file is then run through a classification program using the Random-Forest algorithm (Figure 5). This classifier analyzes each pixel based on its spectrum and surroundings.

 

David Bergvelt and Max Li, under the supervision of David MayerichLed by Professor Bhargava at Bioimaging Science and Technology group, Beckman Institute, University of Illinois at Urbana-Champaign

Digitizing cancer pathology research

Acknowledgments

We would like to thank Professor Bhargava for giving us the opportunity to work alongside his Bioimaging group, and also we would like to thank David Mayerich for giving us the chance to try our hands at programming, and guiding us along our way.

We would also like to thank David Bergandine for sponsoring us and advising us throughout the I-STEM program, and Ms. Williams and Mrs. Destefano for organizing the I-STEM program.

Experience

We found that the hands-on experience of working with programming in C++ helped a lot in learning the language. Learning how to use Git and Cmake, two industry standard applications, will be very useful in the future. It was also a very interesting experience connecting the topics of cancer pathology and statistical analysis together using programming languages.

Working along with Dr. Mayerich in Professor Bhargava’s group gave us the experience of working in a research group doing cutting edge research related how the future of cancer research and diagnosis will look like. Overall, the I-STEM experience was excellent, and we hope to continue working with Dr. Mayerich and the Bhargava group in the future.

Aim Our project focuses on building a C++ program which allows the user to load a spectroscopic image of a tissue sample, feed it into the Random-Forest algorithm, and output a classified image showing differences in tissue type.

The final program will incorporate a graphical user interface, making it user-friendly and useful to a wide range of researchers. We hope that this will encourage researchers to adopt these quantitative methods in their own research and diagnostic practices. In particular, we expect that this type of technology will be useful for accurate disease diagnosis in hospitals.

Fig. 1: Image of a breast tumor biopsy stained using various chemical methods (left) and an image of the same biopsy stained digitally after spectroscopic imaging and classification (right).

Fig. 5: A Random Forest is composed of hundreds of decision trees, where each tree selects a cell type based on a random subset of features of a single spectrum.

Each tree then “votes” for its selected class.

Fig. 4: Each pixel making up the image of the digitally stained cell carries its own infrared spectrum (a and b). The image is the classified into various cell types, such as epithelium, fibroblasts, etc. The accuracy of the classifier is given using the precision: the ratio of cell types that are correctly classified.

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Fig. 2: Schematic of a mid-infrared spectroscopy setup. A detector is used to measure the intensity of the infrared light as it passes through the specimen, the independent variable being the position of a movable mirror.

Fig. 3:The position of the mirror is plotted along with the associated light intensity, and a Fourier Transformation if applied, transforming it into a function of wavelength and intensity.

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Fig. 6: Final result of our classification. The color scale represents the probability of tissue being epithelium tissue, where dark red = very strong probability of epithelium tissue, dark blue = very weak probability of epithelium.

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