Introduction to Digital Biomarkers V1.0

Contiguity, LLC
www.contiguitydx.com
Introduction to Digital Biomarkers
A Technology White Paper
V. 1.0
August 10, 2016
Image Analysis and Classification
Transforming image based data sources into powerful
predictive tools.

INTRODUCTION TO DIGITAL BIOMARKERS
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Contents
Introduction ..................................................................................................................................................3
Classification Algorithms...............................................................................................................................4
Digital Biomarkers.........................................................................................................................................5
Dual Optimization.........................................................................................................................................6
The Classifier Development Process.............................................................................................................7
Developing the Software and Algorithms – The CAMELYON16 Grand Challenge........................................7
Strategy for Meeting the Grand Challenge...................................................................................................8
Conclusions .................................................................................................................................................16
Applications.................................................................................................................................................17
Medicine and Life Science.......................................................................................................................17
Other Industries......................................................................................................................................17
Methods and Acknowledgements..............................................................................................................18
Contacting Contiguity .................................................................................................................................18
References ..................................................................................................................................................19

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Introduction
Contiguity has set out to develop a software suite to digitally identify unique features in image based
data, to use these features “Digital Biomarkers” to assemble multivariate classification algorithms and
to deploy these classification algorithms in an application that can analyze new images and “classify”
them with little to no user intervention.
Key software features include:
1. Analysis of very large images (> 1Gb per image, i.e. BigTIFF files)
2. Allow for an iterative Digital Biomarker and classifier optimization process
3. Rapid development of classification algorithms that are fit-to-purpose, including multi-tiered
decision tree approaches
4. No requirements for deep, image specific subject matter expertise to successfully identify and
employ effective digital biomarkers, and
5. Customizable information output
“Image analysis” is a broad term which includes both the highly specific tasks of machine vision[1] and
the more general methods of pattern recognition[2]. Over the past decade, the continuing
improvement in processor speed combined with the effectively infinite storage capacity of the cloud has
not only made image analysis ubiquitous in consumer applications, but also economically feasible across
a wide range of business and industrial applications[3] [4]. Nearly everyone today carries a computer in
their pocket capable of facial detection, speech recognition, and high speed data transfer (not to
mention phone calls), and terabytes of image data are added to the public and private domains on an
hourly basis. The inescapable conclusion is that image analysis will soon be one of the most significant
expenditures of computational power in the world, if it is not already.
Many current approaches to computer based image analysis largely seek to utilize the same features
and same relationships that humans use when analyzing an image [2]. While this computer assisted
process can provide faster and more consistent results compared to the human-only approach, expert
opinion spans the spectrum on whether it will ever match the performance of the human observer, let
alone exceed it. The only consensus is that such an achievement will be hard.
Another approach that has rapidly gained prominence involves so-called deep learning[5] [3], usually
involving neural networks and other higher-level abstractions of data. The most familiar application of
deep learning is found in the speech recognition ability of modern cell phones, and the same techniques
are being applied to image analysis. Such techniques require very large data sets and the
implementation of “black box” algorithms, often requiring computations that are offset in the cloud.
We focus our approach on extracting features that are obvious to the human eye, as well as usually non-
obvious features found by computer search for local optima. We utilize some deep learning concepts by
integrating the discovery of these features with a recursive, broad scope approach to analytics and
multivariate classifier development. This approach is intended both to match current computer or

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human vision based analysis and to enable new capabilities. Our approach takes full advantage of what
computers are useful for: quantifying, calculating relationships, and considering massive data
combinations in order to meet predefined performance criteria.
Classification Algorithms
Although the discussion below is applicable across a wide range of fields, for clarity it will be restricted
to terms commonly used in the biomedical field. Biomarkers are any biological quantity that can be
measured and therefore assigned a numerical value[6] [7] [8]. Although biomarkers are most
commonly measured from biological fluids, they can also be such quantities as temperature, blood
pressure or body mass index (BMI). The mathematical manipulation of biomarkers to find a diagnosis is
called a classification algorithm[2] [8] [9]. Conventionally, a single biomarker has been used for
diagnosis, for example, the concentration of PSA in blood to diagnose prostate cancer. PSA
concentrations above a certain threshold indicate some likelihood for cancer. Clearly, using more than
one biomarker relevant to a particular disease should give better results, but how to combine the
measurements?
The simplest way to combine biomarkers would be a “voting” algorithm, so that if two out of three
biomarkers indicated positive for disease, for instance, and one indicated negative, the combined result
would be positive. But this method fails to take into account the relative strength of the different
biomarkers. If the negative biomarker was considered a much more reliable indicator than the two
positive biomarkers, the combined result should perhaps be negative, not positive.
A linear combination of the biomarker concentrations is the simplest way to take the relative
significance into account[10]:
L = a A + b B + c C + …
Where L is the combined score, A, B, and C are the various biomarker concentrations or levels, and a, b,
and c are coefficients which may be positive or negative. When L is above a predetermined level (or
within a predetermined range), the result is positive, when L is below, negative. In effect, this method
amounts to a weighted voting algorithm. When the coefficients are determined by a specific
combination of the biomarker averages and standard deviations across the positive and negative disease
states, the above equation is known as a Fisher Linear Determinant [2]. In practice, the Fisher
coefficients are often used as a starting point to find a more optimal determinant, often using ROC curve
analysis[11] [12] [13].
Although there are many possible classification algorithms available, (Bayesian, Support Vector
Machines, Primary Component Analysis, etc. [1] [2] [5]), we have found the Linear Determinant
algorithms to be comparably as powerful as more sophisticated methods for the problems we have
addressed. Linear Determinants have the added advantages of simplicity and fewer parameters,
reducing the dangers of over fitting.

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Digital Biomarkers
Features are extracted from images in our method using very simple (and therefore computationally
non-intensive) region growing methods, in which contiguous pixels are tested for similarity, after first
passing simple thresholding tests. Pixels that are similar enough are grouped into clusters [4]. The
resulting pixel clusters) are then subject to population based tests (number of pixels, pixel density).
The selection of features from an image relies on the algorithms described briefly above, which
themselves rely on several adjustable parameters (our own algorithms have as few as four and as many
as fourteen parameters, depending on the degree of specificity desired). The changing of these
parameters results in the selection of different features. Population analysis of the feature collections
thus selected (for instance, average size, number, color, etc.) yields numbers that are analogous to
biomarker concentrations in medical diagnostics, and can likewise be used to build classifier algorithms,
and which we therefore refer to as digital biomarkers. An example of features that may be selected,
and subsequently used for the generation of digital biomarkers, is shown in Figure 1 below. The image
on the left is an unprocessed image of a section of lymphatic tissue which is negative for cancer. The
dark ovoid features are cell nuclei. Judicious choice of the feature-selection parameters allow most of
these nuclei to be selected, as shown in the identical image to the right, in which the selected features
have been highlighted in various colors. The population statistics of all these selected nuclei are what
are used as digital biomarkers.
Figure 1. An example of features extracted from a non-lesion histology slide to produce digital biomarkers.
The advantages of digital biomarkers over conventional biomarkers are two-fold. First, conventional
biomarkers suffer from variability due to variations in sampling, preparation, storage, and analysis. Most
of these variations have a component of human variability. Although digital biomarkers have to contend
with differences in cameras, exposure, magnification, dyes, etc., these sources of variability are much
easier to control or adjust for. Digital biomarkers are therefore much less subject to preparation
variability than conventional biomarkers. The second advantage of digital biomarkers is more significant
and compelling. Unlike conventional biomarkers, which are “found” quantities, digital biomarkers are to
a large extent the product of the adjustable parameters used to find them. This means they can be

6
optimized for the task at hand. Some part of this optimization happens as a matter of course, through
human adjustment of the parameters to pick out obvious features. However, application of basic
optimization techniques (such as the Downhill Simplex method [14]), allows digital biomarkers to be
tuned by computer from a human-selected starting point that shows good performance, to the nearest
local maximum of improved performance.
Dual Optimization
The classification algorithms discussed above combine the data from several biomarkers in ways that
optimize the accuracy for diagnostic purposes (medical or otherwise). With conventional biomarkers,
that is the best that can be hoped for. However, the use of digital biomarkers allows for another layer of
optimization, as the biomarkers themselves may be optimized for improved diagnosis. As with the
classifier algorithm as a whole, the individual biomarkers can be optimized using ROC curve analysis [11]
[12] [13]. In actuality, the biomarkers are not optimized individually, but optimized in sets derived from
each feature collection, which are in turn determined by the parameters set for feature determination.
This process is shown in Figure 2 below.
Figure 2. Schematic of process used to develop image classifier algorithms from digital biomarkers.

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The Classifier Development Process
The software for developing the classifier algorithm combines many functions. First is the development
of the patterns from which the digital biomarkers are derived. This is initially a visual process, as shown
in Figure 1, but the visually determined starting points are then optimized computationally. The digital
biomarkers thus obtained are then moved through a multivariate classifier discovery process, in which
different combinations of the digital biomarkers are explored. The effectiveness of these classifiers is
determined against different positive and negative image classes.
Once the multivariate classifiers have been developed, they are placed into a decision-tree-like structure
in order to separate out all the different classes of image samples. Technically, these are not decision
trees, as node loops are allowed for calculation efficiency, but it is theoretically possible to recombine
these node loops to regain a purely binary branched decision tree. The size of the image samples and
the sampling frequency are chosen, and the large WSI (typically 2-4 gigabytes) is segmented and
classified. The results are coded into an XML file that can be read by the ASAP [15] software, or
analyzed offline in our own software.
Developing the Software and Algorithms – The CAMELYON16 Grand
Challenge
Contiguity sought out a data set that could be used as a component in the development and testing of
our software suite. The data set that was chosen comes from the Camelyon16 Grand Challenge [15].
This data set was used to develop and test the ability to:
1. Analyze very large images (> 1Gb per image)
2. Apply an iterative digital biomarker and classifier optimization process
3. Develop fit-for-purpose classification algorithms including multi-tiered decision tree
approaches
4. Demonstrate that the software can support development of digital biomarkers and
classification algorithms by personnel without deep subject matter expertise.
5. Effectively analyze new images that the software is naïve to and produce classification data
about the image. In this case, the classification data was the location of tumor lesions.
The CAMELYON16 challenge is entitled “ISBI Challenge on Cancer Metastasis Detection in Lymph Node”
and is one of the Grand Challenges in Biomedical Image Analysis put forth by the Consortium for Open
Medical Image Computing[16]. The CAMELYON16 website provides whole-slide images (WSIs) of
sentinel lymph node sections collected from Radboud University Medical Center, and the University
Medical Center Utrecht. Both universities and CAMELYON16 consortium are located in the Netherlands.
160 normal slides and 110 slides with metastases were included in the data set. Each metastases WSI
was paired with an XML file containing annotation contours delineating the metastasized areas,
prepared by professional pathologists.

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Contiguity has registered with CAMELYON16 for the purpose of gaining access to the datasets in order
to develop and introduce new tools and technologies. The deadline for submission passed in April 2016.
New submissions are still being accepted for evaluation purposes (without public posting of results), and
Contiguity intends to submit as resources permit.
Strategy for Meeting the Grand Challenge
The multi-gigabyte size of the images from the Camelyon16 Grand Challenge prohibits the full analysis of
a histology slide in a timely manner. Instead, our method depends on sampling the image along a grid,
pulling out image segments that typically total 3% - 12% of the area of the full image.
Regions representing bare glass or adipose tissue are quickly eliminated with a filter that tests for
grayness, so that image segments with average colors ranging from white to black, or shades of gray in
between, do not undergo time-consuming testing. Many of the remaining segmented images may be
classified as negative using a simple and fast color filter, as negative tissue regions are more densely
populated with muclei inconsistent with tumors. The remaining image segments are subject to the full
multivariate classifier treatment, usually utilizing cascading branches of classifiers in a decision tree,
ultimately deciding whether a particular image segment is classified as positive or negative. If an image
segment is found to be positive, the region around it is searched and classified exhaustively, providing
full coverage of that region.
Figure 3 shows a section of a tumor-positive histology slide. The regions demarcated with the dark blue
line are those judged by histologists associated with CAMELYON16 to be tumor positive. The light blue
squares indicate regions judged by our algorithm to be tumor negative, via color pre-filter. The squares
do not indicate the actual size of the region analyzed, but rather the center of the region. The actual
region analyzed extends one third of the distance to each neighboring indication square. The red
squares indicate tumor-positive regions as determined by the classifier decision tree, while the yellow
regions indicate tumor-negative regions as determined by the classifier decision tree.

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Figure 3. A region of a lymph node histology slide showing a tumor lesion (delineated by a dark blue border). Blue squares
indicate the center points of sampled regions which a color filter has determined to be negative. Red squares indicate the
center points of sampled regions which a digital biomarker classifier has determined to be positive, while yellow squares
indicate regions classified as negative. The larger spacing to the left is indicative of the initial sample spacing, while the tighter
spacing in the lesion region is indicative of the total coverage used when positive samples are found.
The Camelyon16 Grand Challenge employed a two-tier evaluation process. A test set consisting of 130
WSI (both positive and negative, blinded) were used for the evaluation. The first tier asked merely to
provide the probability that the provided images were positive for metastatic tumor lesions. The second
tier asked for the location of the lesions within each image, along with the confidence that the
determined locations indicated an actual lesion. Due to time and resource constraints, as a preliminary
step, we chose to analyze 8 known tumor WSIs and 11 known normal WSIs, all of which were naïve to
the training process. Images were chosen to cover the spectrum of images found in the training set. In
particular, the number of lesions per image varied from a single lesion to in excess of 40 lesions, with
the size of the lesions varying from 0.01 megapixels to more than 1400 megapixels.
For the purposes of classifying discovered lesions as true or false positives (TP or FP), or true or false
negatives (TN or FN), it was necessary to define what qualified as a valid lesion (therefore TP or FN,

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depending on how classified), and what qualified as a positive lesion (TP or FP). Nearly all of the smaller
lesions were satellite lesions to larger lesions – in close proximity or even touching the major lesions. In
most cases, these were clearly artifacts of the “ground truthing” process, and not lesions in their own
right, often not big enough to hold even a single tumor cell, and definitely smaller than the image
sample size. Because all positive WSIs contained at least one lesion larger than 1 megapixel, and
because the diameter of a 1 megapixel lesion is roughly equivalent to the sample spacing we prefer to
use, we set 1 megapixel to be the lower bound size for a valid lesion.
Image samples which classify as false positives are fairly common, and it is desirable to eliminate these.
One method is to only count these samples as positives if they cluster in numbers above a certain
threshold. As shown in Figure 4, raising the threshold for the number of contiguous pixels in a cluster up
to four allows for perfect image specificity (correctly selecting out all 11 negative images) at some
expense of image specificity (only selecting 6 of the 8 positive images correctly). However, perfect
image sensitivity proves to be impossible without resorting to much more sophisticated techniques, as
one of the positive images only contains one valid lesion, which is largely made up of cell-free inclusions
that the algorithm declines to classify (see Figure 5). As seen in Figure 4, high sensitivity for the lesions
is difficult to achieve, without denser sampling due to the small size of many lesions. Better results are
achieved by executing a complete sampling around positive points as shown in Figure 3.
Figure 4. Image and lesion sensitivity and specificity versus positive cluster size. Open red squares are image sensitivity. Open
blue square are image specificity. Closed red circles are lesion sensitivity. Closed blue circles are lesion specificity. The
increased difficulty of detecting positive lesions over positive images is apparent in the data. Setting the cluster threshold to
four allows for perfect image specificity, but lowers the image sensitivity to 6/8.
0
0.2
0.4
0.6
0.8
1
1.2
1 2 3 4 5 6 7 8 9 10
Sensitivity,Specificity
Minimum Contigous Points

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Figure 5. The single valid lesion in the positive WSI Tumor_066 has a large cell-free inclusion at the image sample location,
which the algorithm declines to classify, thereby classifying this WSI as a false negative. Note the false positive sample point
(red) to the left of the lesion. This point is eliminated by setting the clustering threshold greater than one.
A high segment-density scan of WSI Tumor_37 was performed and results are shown in Figures 6 – 10.
Sampling was performed as 200 x 200 square pixel regions spaced every 400 pixels across the entire
image to demonstrate the ability to identify small tumors. Since we are using consumer level processors
and a very high level language, analysis of this particular image required approximately 4 hours of
processing time. This processing time can be reduced by a factor of 100 or more by employing a
number of readily available technologies that are outside the scope and budget of this immediate study.

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Figure 6. WSI Tumor_37 with supplied tumor annotations (dark blue) and rectangular annotations showing regions magnified
as; Figure 8-Yellow, Figure 9-Green and Figure 10-Light Blue. Image file size is 2.6 Gb. Included Image Scale Bar equals 5mm.

13
Figure 7. WSI Tumor_37 with supplied tumor annotations (dark blue and difficult to see) and segments analyzed by Contiguity
software. The light blue segments represent regions screened out as highly unlikely to contain tumor cells and the yellow
segments were fully analyzed regions classified normal and red segments represent likely tumor regions. Result overview is;
Total Positive Points: 868, Total Negative Points: 11732, Number of true positives: 145, Number of false positives: 723,
Number of true negatives: 11670, Number of false negatives: 62. Included Image Scale Bar equals 5mm.

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Figure 8. Region defined as yellow in Figure 6 showing recognition of multiple tumor regions by red squares and non-tumor
regions by yellow and light blue squares. Included Image Scale Bar equals 1mm.

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Figure 9. Region defined as green in Figure 6 showing recognition of multiple tumor regions including very small tumors by red
squares and non-tumor regions by yellow and light blue squares. Included Image Scale Bar equals 1mm.

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Figure 10. A full slide image showing all true positives and false positives. Certain types of lymph node medullary tissue are
problematic at the microscale. Contiguity is confident that this issue can be accurately classified in the future by introducing
and optimizing additional digital biomarkers, building classifiers against smaller image segments to ensure a higher degree of
training image homogeneity and introducing additional classification levels to decision trees which utilize more macroscale
features of an image which tend to be considerably different than tumor containing regions. Included Image Scale Bar equals
4mm.
Conclusions
Contiguity undertook this challenge as part of an image analysis software development process. We
have made significant progress developing a suite of software tools that can be used to classify and
analyze a very broad range of images. Early results are promising; there is more work to do in order to
provide an application that can fully meet the goals of the Camelyon16 Grand Challenge.
Contiguity devoted 8 weeks to this process. This included writing and testing the software, developing
algorithms, testing and optimization. We are continuing to develop tools that will improve and
accelerate this classification process. Developing algorithms that demonstrate both high sensitivity and
high specificity appears possible based on current data and rate of progress. What is clear is that this is
a difficult challenge due to the high degree of heterogeneity demonstrated across images from this
challenge and that Digital Biomarkers can be employed to identify and classify specific tissue types
across very large and heterogeneous images.

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Future work will include:
1. Continue optimizing broader pattern finding techinques which will enable us to produce more
effective classifiers.
2. Improve the speed of analysis through;
a. Enabling the software to utilize 64 bit processors and graphics cards
b. Modify the architecture to take advantage of multiple processors
c. Convert the computationally intensive processes to assembly language or similar.
3. Improving speed will;
a. Improve sensitivity to small tumors by simply analyzing more of the image,
b. Set the software on a path towards being an effective tool to be used in large studies or
in histology labs.
4. Integrate all elements of the software suite to;
a. Enable rapid optimization and modification of decision trees,
b. Enable a rapid recursive process whereby false positives and negatives are identified
and used to enhance existing classifiers as well as add new decision tree branches.
Applications
Medicine and Life Science
Although this white paper has focused on medical imaging, specifically on the diagnosis of tumor
metastasis in lymph nodes, it should be clear that these same methods have applicability across a wide
range of fields. Most any sort of microscopy, laboratory imaging analysis or other medical image based
tools such as MRI, CAT Scan, X-Ray to name a few could benefit from these techniques. For instance:
 Discover situation specific biomarkers, custom biomarker identification and deployment in support of
clinical trials.
 Develop more effective and efficient sorting and staging algorithms
 Discover and deploy new biomarker and biomarker relationships with multi-index classifiers to achieve
significant improvements in sensitivity or specificity.
 If your clients are requesting a new analytical capability, our approaches may enable you to offer a new
high-value service.
 Deep analysis of therapeutic or procedural effects
 Applications that enable quick but well characterized biomarker based disease classifier identification
and verification.
Other Industries
Many quality assurance tools already make extensive use of imaging. New tools could be developed, or
old tools extended for:
 Monitoring material properties.
 Weld joint analysis.
 Automated checks of in-process manufacturing, packaging, and material acceptance.

18
 Increasing the capabilities of SPC by discovering and employing non-obvious relationships and metrics
available in existing processes.
 Automated image and data inspection.
 Chasing down indicators for manufacturing defects.
Methods and Acknowledgements
TIFF (Tagged Image File Format [17]) images are normally limited to 4 GBytes. Although all the histology
images we analyzed were in the 1-3 GBytes range, the Consortium for Open Medical Image Computing
has shown foresight by curating all images in the BigTIFF format[18], which in theory allows image sizes
of up to 16 million Terabytes. Big TIFF files cannot be opened by most commercial imaging software.
Automated Slide Analysis Platform (ASAP) [19] is an open source platform for visualizing, annotating and
automatically analyzing whole-slide histopathology images. ASAP is built on top of several well-
developed open source packages like OpenSlide, Qt and OpenCV[15]. We have relied on ASAP for
viewing and annotating the Camelyon16 images, and TIFFFASTCROP[20] for segmenting these original
images into JPEG images of a size we could analyze (typically 200-500 pixels square). TIFFFASTCROP was
developed by the modelling team of the IMNC laboratory near Paris, France.
The image analysis software was written in MicroSoft’s C# language using Visual Studio 2013. The
classifier software was written in Java using Eclipse Europa. Software was run on either a Lenovo
ThinkPad T430 having an Intel® Core™ CPU running at 2.50 GHz, 8 GB RAM, running Windows 7
Professional, or a laptop comupter having an Intel® 6th
Generation Core™ CPU running at 2.50 GHz, 16
GB RAM, with an NVIDIA GeForce 940M video card, running Windows 10.
Contacting Contiguity
Web: www.contiguitydx.com
Phone: (888) 913-3915
Email: steve.tyrrell@contiguitydx.com

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