Assessing Red Blood Cell Deformability using Deep Learning

Abstract

Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of these cells. A variety of methods have been developed to measure RBC deformability, but these methods require specialized equipment, long measurement time, and highly skilled personnel. To address this challenge, we investigated whether a machine learning approach could be used to predict donor RBC deformability based on morphological features from single cell microscope images. We used the microfluidic ratchet device to sort RBCs based on deformability. Sorted cells are then imaged and used to train a deep learning model to classify RBC based image features related to cell deformability. This model correctly predicted deformability of individual RBCs with 81 ± 11% accuracy averaged across ten donors. Using this model to score the deformability of RBC samples was accurate to within 10.4 ± 6.8% of the value obtained using the microfluidic ratchet device. While machine learning methods are frequently developed to automate human image analysis, our study is remarkable in showing that deep learning of single cell microscopy images could be used to assess RBC deformability, a property not normally measurable by imaging. Measuring RBC deformability by imaging is also desirable because it can be performed rapidly using a standard microscopy system, potentially enabling RBC deformability studies to be performed as part of routine clinical assessments.

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Cite this: DOI: 10.1039/d1lc01006a
Received 6th November 2021,
Accepted 27th November 2021
DOI: 10.1039/d1lc01006a
rsc.li/loc
Assessing red blood cell deformability from
microscopy images using deep learning†
Erik S. Lamoureux,
ab
Emel Islamzada,
bc
Matthew V. J. Wiens,
d
Kerryn Matthews,
ab
Simon P. Duffy
abe
and Hongshen Ma *
abdf
Red blood cells (RBCs) must be highly deformable to transit through
the microvasculature to deliver oxygen to tissues. The loss of RBC
deformability resulting from pathology, natural aging, or storage in
blood bags can impede the proper function of these cells. A variety
of methods have been developed to measure RBC deformability, but
these methods require specialized equipment, long measurement
time, and highly skilled personnel. To address this challenge, we
investigated whether a machine learning approach could be used to
predict donor RBC deformability based on morphological features
from single cell microscope images. We used the microfluidic
ratchetdevicetosortRBCsbasedondeformability. Sorted cells are
then imaged and used to train a deep learning model to classify RBC
based image features related to cell deformability. This model
correctly predicted deformability of individual RBCs with 81 ±11%
accuracy averaged across ten donors. Using this model to score the
deformability of RBC samples was accurate to within 10.4 ±6.8% of
the value obtained using the microfluidic ratchet device. While
machine learning methods are frequently developed to automate
human image analysis, our study is remarkable in showing that deep
learning of single cell microscopy images could be used to assess
RBC deformability, a property not normally measurable by imaging.
Measuring RBC deformability by imaging is also desirable because it
can be performed rapidly using a standard microscopy system,
potentially enabling RBC deformability studies to be performed as
part of routine clinical assessments.
Introduction
Red blood cells (RBCs) are highly specialized cells that
facilitate tissue respiration by delivering oxygen and removing
carbon dioxide.
1,2
RBCs transverse through the entire
circulatory system approximately every 60 seconds. Their
journey includes the microvasculature, where RBCs must
deform through capillaries measuring as little as 2 μmin
diameter, as well as the inter-endothelial clefts of the spleen
measuring 0.5–1.0 μm in diameter.
3,4
The loss of RBC
deformability, due to pathology, natural aging, or storage in
blood bags, reduces the ability of RBCs to circulate and
facilitate their removal from circulation by phagocytes in the
spleen and the liver.
5,6
As a result, there is significant interest
in methods for measuring RBC deformability as a potential
biomarker for diseases, such as malaria
7
and
hemoglobinopathies,
2,8
or for assessing the quality of donated
RBCs for use in blood transfusions.
9,10
Approaches for measuring RBC deformability can be
classified as either flow-based or deformation-based
methods. Flow-based methods deform RBCs using fluid
shear stress and then measure the resulting shape change. A
classical method is ektacytometry, which deforms RBCs using
shear flow between two transparent cylinders and then uses
optical diffraction to measure the resulting population RBC
elongation.
11,12
Other flow-based methods deform RBCs
using high shear flow through microchannels and then
measure the resulting RBC elongation using high speed
imaging
13,14
or electrical impedance.
15
Classical deformation-
based methods include micropipette aspiration,
16
atomic
force microscopy,
17
and optical tweezers,
18,19
which measure
RBC deformability via single cell manipulation, and require
complex experimentation, skilled personnel, and specialized
equipment.
9
Microfluidic deformation-based methods
measure RBC deformability via capillary obstruction,
20
deposition length in tapered constrictions,
21,22
transit
pressure through constrictions,
7,10,23–27
transit time through
constrictions,
28–30
and sorting RBCs based on deformability
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a
Department of Mechanical Engineering, University of British Columbia, 2054-
6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
E-mail: hongma@mech.ubc.ca
b
Centre for Blood Research, University of British Columbia, Vancouver, BC,
Canada
c
Department of Pathology and Laboratory Medicine, University of British
Columbia, Vancouver, BC, Canada
d
School of Biomedical Engineering, University of British Columbia, Vancouver, BC,
Canada
e
British Columbia Institute of Technology, Burnaby, BC, Canada
f
Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, BC, Canada
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using microfluidic ratchets.
31–33
A common challenge for all
existing deformability assays is the needs for specialized
apparatus and skilled personnel, which limit the ability to
translate the technology to clinical settings.
34
Additionally,
since different assays rely on different underlying principles
for measuring RBC deformability, it is often difficult or
impossible to compare results across studies.
As an alternative to physical measurement of RBC
deformability, cues for biophysical changes in these cells
may be obtainable from microscopy images, without the need
for highly specialized equipment and personnel. RBCs
typically exhibit a highly deformable biconcave discoid
morphology, and deviation from this morphology may
correspond with changes in cell deformability.
35,36
In fact,
deep learning methods have been developed to assess
changes in RBC morphology during cold storage,
37
malaria,
38–43
sickle cell disease,
44–49
and thalassemia.
50–52
However, RBC morphology varies over the life cycle of the cell
and this variability may obscure efforts to infer deformability
from cell morphology. Furthermore, no specific
morphological features can be directly attributed to
predictable changes in RBC deformability. We recently
developed a microfluidic process for deformability-based
sorting of RBCs
31–33
as well as a deep learning method to
distinguish cell lines based on feature differences
imperceptible to human cognition.
53
We hypothesized that a
combination of these advances could enable the indirect
measurement of RBC deformability via cell imaging from
optical microscopy to identify image-based cell
morphological features associated with deformability.
Here, we investigate the potential to use deep learning to
assess RBC deformability based on cell morphological
features from brightfield microscopy images. We leverage the
ability of a microfluidic ratchet device to sort RBCs based on
deformability to generate training sets of RBCs with distinct
deformability. We show that the deep learning model can
classify RBCs into deformable or rigid fractions using donor
dataset sizes ranging from 20 000 to 70 000 images. For a
sample of ten donors, who were diverse in terms of blood
type and sex, testing classification accuracy ranged from 64–
95% with an aggregate mean (± SD) of 81 ± 11%. Using our
model to predict donor RBC rigidity scores (RS) was accurate
to within a mean of 10.4 ± 6.8% deviation, compared to
measurement using a microfluidic device. Our results
confirm that RBC deformability can be assessed from
microscopy images to potentially simplify the assessment of
RBC deformability.
Results
Approach
Our experimental approach (Fig. 1) involves first using the
microfluidic ratchet device to sort RBCs based on
deformability in order to acquire training data for a deep
learning image classification model. The microfluidic ratchet
device (Fig. 1A) sorts RBCs based on deformability by
squeezing cells through a matrix of tapered constrictions
(described in Fig. 2). After microfluidic sorting (Fig. 1A),
RBCs from each outlet are extracted from the microfluidic
device and placed in wells on a 96-well plate (Fig. 1B). These
wells are then imaged using an optical microscope in
brightfield using a 40×objective (Fig. 1C and D). The
microscopy images are processed and segmented into single
cell images (Fig. 1E). The resulting datasets are then used to
train and test a convolutional neural network for
deformability-based image classification (Fig. 1F). Finally, the
trained deep learning model is used to classify RBCs in a test
set based on deformability. The aggregate RBC deformability
is also used to obtain a deep learning-derived deformability
Fig. 1 Overall experimental approach. (A) Deformability based sorting using the microfluidic ratchet device. (B) Sorted RBC fractions are
transferred to a well plate for imaging. (C) Brightfield imaging using a 40×objective. (D) Example full well image scan. (E) Examples of individual
segmented RBCs from donor 1. (F) Structure of the convolutional neural network for image-based cell classification. (G) RBC rigidity score
estimated using the CNN. (H) Rigidity score measured by deformability-based cell sorting.
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profile of the RBC sample (Fig. 1G), which is compared to the
profile obtained by microfluidic testing (Fig. 1H).
Deformability based cell sorting
We sorted RBCs based on deformability using the
microfluidic ratchet device described and validated
previously.
7,9,10,16,31–33,54–57
Briefly, RBCs migrate under
oscillatory flow through a matrix of micropores with
openings ranging from 1.5 μm to 7.5 μm (Table 1). The
micropores have the same opening in each row, but
progressively smaller openings along each column
(Fig. 2A and B). When the cells can no longer transit the
micropores along a particular row, they instead flow along
the row and are directed into one of 12 outlets
(Fig. 2B and C). Oscillatory flow within the device dislodges
cells that are blocked by the micropores, ensuring that the
cells do not clog the device, allowing for unimpeded
operation. In this way, the RBC sample is fractionated based
on deformability at a rate of ∼600 cells per minute. More
detailed device design and operational details can be found
in our previous work.
9,55,58
RBC rigidity score (RS)
After deformability-based sorting, the RBC distribution can
be shown as a histogram, where a rightward distribution
corresponds to a more rigid RBC sample (Fig. 3A). To
compare deformability between samples, the RBC
distribution can be shown as a cumulative distribution,
which allows us to define a rigidity score (RS) as the outlet
number where the cumulative distribution crosses 50%.
Fractional outlet numbers can be determined by linear
interpolation between data points greater and less than 50%
in the cumulative distribution function. RBC samples from
different donors showed significant variability in their RS
value. For example, from the ten donors in this study the RS
ranged from 2.47 to 3.50 (Fig. 3B and C).
Donor-based variability in RBC deformability
Blood donations from ten healthy donors were obtained and
RBC samples were sorted based on deformability using the
microfluidic ratchet device (Fig. 3C). The donors were diverse
in terms of blood type and sex (Table 3). Three blood samples
were fresh from a citrate tube (used the day of donation),
Fig. 2 Microfluidic ratchet sorting device operation. (A) Tapered funnel constrictions allow for unidirectional filtration of RBCs upwards through
the constrictions based on their tapered geometry and upward oscillatory flow. Downward oscillatory flow declogs cells that are too rigid to pass
through the constrictions. The constriction width for a row of tapers decreases along the flow path (i.e.,a<b) to enable deformability-based cell
separation. (B) Deformability based sorting occurs in a matrix of tapered constrictions, producing a ratcheting effect. (C) 10×magnification
micrograph of the active sorting region where cells are routed to different deformability outlets. (D) 4×magnification micrograph of the entire
microfluidic sorting region. RBCs are pictured flowing in an upward diagonal direction from the sample flow inlet on the left, through the matrix of
constrictions, and are primarily routed to outlets 3 and 4 on the right.
Table 1 Constriction size for each outlet number
Outlet 1 2 3 4 5 6 7 8 9 10 11 12
Size (μm) 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 4.50 5.50 7.50
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four were fresh from a blood bag (≤3 days after donation),
one was in the second week of blood bag storage, one was in
the second week of tube storage, and one was stored in a
blood bag for just over 3 weeks (Table 3). Donor RBCs were
sorted into outlets 1–6, with the majority (>99%) sorted to
outlets 2–5. The cumulative distribution of sorted cells to
each outlet is presented in Fig. 3C. These deformability
curves and RS are donor-specific and can be reliably
measured in repeated experiments using replicate
microfluidic devices.
9
The donor RS range from 2.47–3.50,
which is similar to previous results where the RS ranged from
2.36–3.69 for fresh blood and blood stored for one week
could reach elevated RS scores of 3.74.
9
Optical microscopy imaging for deep learning
After deformability-based cell sorting, the sorted cells are
extracted from the microfluidic device by pipetting and
placed in 96-well imaging plate (Fig. 1B). Samples from each
outlet were split in half and placed in two wells to introduce
additional variance in the imaging conditions. These
variations include a greater variety of light conditions based
on location of cells in the well, cells with different
thicknesses of suspension fluid due to its meniscus, and
different imaging conditions resulting from differences in
focus and exposure time. Full image scans were conducted
on each well using a 40×objective and a DS-Qi2 camera on a
Nikon Ti-2E inverted microscope, capturing brightfield
images of 2424 ×2424 pixels (Fig. 1C and D). Image captures
near the edge of the wells were often out of focus and were
discarded prior to segmentation.
Segmentation
To perform deep learning classification of individual RBCs,
we developed a Python program to extract 60 ×60 pixel image
patches each containing a single RBC (Fig. 1E). Cells are
located using a Sobel operator for edge detection and Otsu
multi-thresholding. A centre of mass measurement is
Fig. 3 Microfluidic deformability-based sorting results. (A) RBC distribution after deformability-based sorting for select donors. Donor 2 is the
most deformable sample (orange) and donor 4 is the most rigid sample (green) of the ten donors analyzed. (B) Cumulative distribution of RBC
deformability from donor 2 and 4. The rigidity scores (RS) are measured at the 50% crossover of the cumulative distribution. (C) Cumulative
distributions and RS for all ten donors.
Table 2 Number of unique segmented single cell images in each outlet
for all ten donors. From these datasets, deformable (outlets 2 and 3) and
rigid (outlets 4 and 5) images are split for training and testing, and are
subsequently augmented for class balancing
Donor
Number of images
Outlet 2 Outlet 3 Outlet 4 Outlet 5
1—14 655 13 831 —
2 7566 14 099 7195 —
3 7849 15 352 14 152 3448
4—5345 3634 13 482
5—10 583 9879 —
6 7931 26 514 26 407 10 606
7—8717 25 455 5938
8 9845 18 124 16 776 6948
9—17 094 13 231 —
10 —24 276 26 811 18 789
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conducted to centre the identified cell for image cropping.
After the cells are identified and cropped, they proceed
through a selection algorithm that keeps images with a single
cell centered in the image and rejects images with multiple
cells. Further, the resulting selected cropped images are
manually audited and any remaining images with multiple
cells or those out of focus are removed. This segmentation
procedure resulted in datasets containing 20 000 to 70 000
single cell images for each donor (Table 2). The segmentation
process was a significant bottleneck in our analysis process,
with the manual data cleaning procedure requiring multiple
hours of manual auditing per donor. This bottleneck could
potentially be addressed in future iterations of this approach
by implementing a U-Net algorithm for pixel-based image
segmentation.
59
Network design
We designed a convolutional neural network (CNN) to
conduct image feature extraction and classification using the
Keras library in TensorFlow (Fig. 1F). For feature extraction,
the model utilizes a series of 4 convolutional layers and 3
max pooling layers. We tried a variety of initial kernel sizes
as larger sizes capture more robust image features. We
settled on an initial convolutional layer kernel size of 7 ×7as
larger sizes did not substantially improve performance. The
second convolutional layer kernel size is 5 ×5, and the final
two are 3 ×3. Each convolutional layer is followed by batch
normalization and ReLU activation. The latter classification
section consists of 3 fully connected layers and a final
smaller fully connected output layer. The 3 fully connected
layers are followed by batch normalization, ReLU activation,
and 20% dropout. The output layer uses a SoftMax error
function for backpropagation during training. The network
utilizes a binary cross-entropy loss function and stochastic
gradient descent for optimization. The design of this model
was influenced by the AlexNet model architecture
60
and deep
learning architecture used in previous work in our lab.
53
The
model was modified for 60 ×60 pixel input images and the
number of layers and their sizes were initially iteratively
adjusted based on training time and training and validation
convergence outcomes.
Training and validation
The CNN was trained using single-cell images with true
deformability labels determined by microfluidic
deformability-based cell sorting. The CNN utilized balanced
training classes of 10 000 images per class per donor. Cell
images were augmented by a random integer multiple of 90-
degree rotation to capture different cell orientations and
lighting characteristics (Fig. 1E). Classes with fewer than
10 000 images were up-sampled, and classes with greater
than 10 000 images were sub-sampled. The model was
trained and validated using five-fold cross validation. The
average training accuracies across the five folds for each
donor is shown in Table 3 and Fig. 4D. Using the validation
and its convergence for each fold, the model was evaluated
for hyperparameter tuning iteratively to determine the
optimal learning rate, number of epochs, optimizer type, and
batch size. Learning rates and number of epochs were donor-
specific and ranged from 0.0001 to 0.1 and 25 to 80,
respectively. We settled on a stochastic gradient descent
(SGD) operator with decay 10
−6
and Nesterov momentum 0.9,
as an Adam optimizer did not markedly improve outcomes,
and the SGD hyperparameters were reliable across all donors.
A batch-size of 32 was determined and held for all donors
because larger batches can result in reduced ability to
generalize, and smaller batches can make learning too
stochastic, causing unreliable convergence. There was
significant variation in the model's ability to converge during
training between different donors, illustrated by the variation
in training epochs and learning rates.
Classification
We initially used our CNN to classify cells from microscopy
images based on the outlet they were sorted to. However,
classifying cells in this manner resulted in poor classification
accuracies (Fig. 4A). This result likely derives from there
being substantially fewer single cell images from outlets 2
Table 3 Donor characteristics, deep learning results, and comparison of microfluidic and deep learning determined rigidity scores (RS)
Donor
Blood
type Sex
Storage type, storage
time Outlets
Training
accuracy
b
(%)
Validation
accuracy
b
(%)
Testing
accuracy
c
(%)
Microfluidic
RS
Deep
learning RS
1A−F Fresh, day 0 3, 4 92 93 92 3.27 3.19
2 A+ M Tube, day 12 2, 3, 4 96 95 95 2.47 2.66
3O−M Bag, day 1 2, 3, 4, 5 86 83 84 2.96 2.84
4 A+ M Bag, day 2 3, 4, 5 99 96 83 3.50 3.41
5 B+ F Bag, day 2 3, 4 86 84 82 3.15 3.18
6 B+ M Bag, day 3 2, 3, 4, 5 76 67 64 2.96 3.05
7 B+ M Fresh, day 0 3, 4, 5 85 75 71 3.47 3.24
8 B+ F Bag, day 9 2, 3, 4, 5 80 68 66 2.77 2.75
9O
a
F Fresh, day 0 3, 4 96 93 95 3.27 3.22
10 B+ F Bag, day 23 3, 4, 5 87 83 81 3.25 3.11
a
Donor Rhesus factor unknown.
b
Training and validation accuracies indicate the average accuracy across the five cross validation folds.
c
Additional testing metrics (including precision, recall, F1-score, and ROC AUC) are tabulated in Table S1.†
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and 5, compared to outlets 3 and 4 (Table 2). To create
balanced classes of 10 000 images, substantial up-sampling
was performed on cells from outlets 2 and 5, requiring many
repeated cells. As a result, the variety of cell images seen by
the model for these classes were significantly limited.
Interestingly, more misclassification occurred between
adjacent outlets (Fig. 4A), indicating that the model was
learning some common deformability-based cell features.
To improve our classification accuracy, we collapsed the
image data from outlets 2 and 3 together and outlets 4 and 5
together to create classes of deformable and rigid cells. By
collapsing the classes in this manner, the datasets are more
robust as additional cell images are available for
augmentation, which required less up-sampling. This binary
classification method resulted in substantially improved
deformability image predictions (Fig. 4B). An additional
advantage of this binary classification method is that inter-
donor comparisons are more appropriate as all donors have
cells sorted to outlets 3 and 4, but not all have cells sorted to
outlets 2 or 5. Since differences in deformability is not a
feature that can be easily discerned by a human observer, a
large and robust dataset is required for training and testing
(Fig. 4C). Using this classification scheme, training
accuracies ranged 76–99% and final validation accuracies
ranged 67–96%, shown in Table 3 and Fig. 4D.
Testing
The testing datasets are comprised of 20% of the overall
segmented data and were separated from the training and
validation sets prior to augmentation. This process ensured
that there are no cell image repeats between the training and
testing sets. For each donor, the images were augmented
using the same method used in training and were up-
sampled or down-sampled to obtain balanced testing sets of
2000 images per class. Among our donor population, we
observed testing accuracies ranging 64–95% with an
aggregate mean (± SD) of 81 ± 11% (Table 3 and Fig. 4D). For
each donor, we observed that testing accuracy corresponded
well with validation accuracy. Additional testing metrics,
including precision, recall, F1-score, and ROC AUC can be
found in Table S1.†
Fig. 4 (A) Normalized confusion matrix for donor 3 when RBCs from outlets 2–5 were considered as separate classes. (B) Normalized confusion
matrix for donor 3 when RBCs from outlets 2 and 3 (deformable) were pooled into a single class, and RBCs from outlets 4 and 5 (rigid) were
pooled into a single class. Classification accuracy is greatly increased with outlets pooled. (C) Example images from outlets 2–5 for donor 6. There
does not seem to be obvious visual differences in the RBCs from different outlets. (D) Image classification training, validation, and testing
accuracies for all donors. Training and validation accuracies are averaged over the five folds.
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Saliency maps
A key consideration for image classification using deep
learning is whether classification was driven by imaging
artifacts, such as lighting, sample preparation, image
acquisition parameters, and position in the imaging well.
61
To resolve this potential issue, we generated saliency maps to
assess if the model learned relevant cell morphological
features. A saliency map is a visual representation of the
spatial support of a particular class to indicate which pixels
in a given image had the greatest effect on the classification
probability.
62
As shown in Fig. 5, the saliency map shows that
pixels having the most influence on classification
corresponded to those clustered around the cell itself,
especially distinct cell features, rather than surrounding
regions. This result confirms that our model is classifying
RBC deformability based on cell morphological features.
Model performance analysis
To further confirm the model performs image classification
using cellular features and is not strongly influenced by
artifacts, we investigated how classification accuracy varies
with reduced imaging resolution. Images were down-sampled
from 60 ×60 pixel images to mimic imaging at lower
resolutions. Classification accuracy were found to reduce
predictably with increased down-sampling (Fig. S3†).
We further investigated how classification accuracy varies
with reduced training data. Classification accuracy were
found to decrease predictably with reduced training data
(Fig. S4†). Specifically, our results indicate that ideally >5000
images per class are required to obtain robust classification
accuracy.
Finally, we performed a Hough circle transform analysis
on cells from different deformability outlets to investigate
whether deformability could be determined from planar cell
size. We found planar cell size were invariant to cell
deformability as determined by sorting, but interestingly,
there were statistically significant differences in cell size
between donors, which confirmed the validity of our Hough
circle transform analysis (Fig. S2†).
Using deep learning to determine rigidity scores of RBC
samples
By classifying RBCs into deformable and rigid fractions, we
can use this result to estimate the RS for each RBC sample.
RBCs classified as deformable and rigid classes are assigned
to outlet 3 and 4, respectively. This scheme is justified for the
ten donors studied here since the vast majority (86%) of cells
from all these donors were sorted into outlets 3 (39%) and 4
(47%), with the reminder sorted into outlets 2 (5%) and 5
(9%). This approach also ensured the RS calculations are
consistent for all donors as not every donor had cells sorted
Fig. 5 Original images (left), saliency maps (centre), and saliency maps with smoothing (right) of randomly sampled RBCs from donor 4. The
saliency map indicates the strength of pixel contributions to the final classification output by the CNN. Warmer colours indicate greater
contribution and cooler colours indicate lesser contribution.
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to outlets 2 or 5, but all donors had cells sorted to outlets 3
and 4. After classification, the RS is then calculated as before
by linearly interpolating the cumulative distribution
deformability curve to find the outlet number at the 50%
crossover frequency. Assigning cells from outlets 2 and 3 to
outlet 3, outlets 4 and 5 to outlet 4 can potentially
overestimate the RS for deformable samples and
underestimate the RS for rigid samples, but this error is
small and systematic since the majority of the RBCs from
healthy donors are sorted into outlets 3 and 4.
Comparing the cumulative distributions and RS obtained
by cell sorting using the microfluidic ratchet device with the
RS estimated by deep learn showed a strong agreement
(Fig. 6). Specifically, the measured and estimated RS values
deviated between a minimum 0.02 (donor 8) to a maximum
0.23 (donor 7) with a mean of 0.10 ± 0.07. Using the potential
deep learning RS range between 2.50 and 3.50, the resulting
mean percent deviation between the deep learning estimated
RS and the microfluidic RS across the ten donors is 10.4 ±
6.8%. Previous work with this microfluidic device has shown
a standard deviation for RS of 0.17 across five different tests
on the same sample.
9
Expressed differently, this RS deviates
by 13.8%, relative to the RS range across all donors in that
work, indicating the level of deviation seen here is within
acceptable variation in RS resulting from random sampling
and manufacturing. Furthermore, we plotted the RS acquired
by deep learning against RS acquired by microfluidics for the
ten donors and found a high degree of correlation between
Fig. 6 (A–J) Comparison of microfluidic (solid lines) and deep learning (dashed lines) derived RBC deformability cumulative distributions and RS
for all ten donors. (K) Relationship between rigidity scores determined by microfluidics and deep learning methods for all ten donors. The deep
learning RS values are strongly correlated to the microfluidic RS values (r= 0.94) and this relationship is statistically significant (p<0.0001).
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the two methods (Fig. 6K), with a Pearson's correlation of r=
0.94 and p<0.0001.
To assess whether cell sorting using the microfluidic
ratchet device may have altered the RBCs, we used the donor-
specific trained CNN to classify unsorted cells from donor 2,
3, and 4. This test also assessed the model's generalizability
by applying a testing dataset acquired in a separate
experimental procedure from the training data. Donors 2, 3,
and 4 were selected for assessment as these donors represent
the full range of donor RS: donor 2 is most deformable (RS =
2.47), donor 3 is in the middle (RS = 2.96) and donor 4 is the
most rigid (RS = 3.50). As before, the cumulative distribution
and RS obtained by cell sorting and deep learning were
similar with the difference in RS for donor 2, 3, and 4 being
0.06 (5.6%), 0.13 (12.6%), and 0.01 (1.0%), respectively
(Fig. 7). In summary, these results show that our deep
learning model is a robust and generalizable for classifying
the deformability of RBCs acquired and processed separately
from the training dataset.
Discussion
This study developed a deep learning method to assess RBC
deformability directly from microscopy images in order to
avoid the use of complex and time-consuming physical
measurements. By sorting RBCs into fractions based on
deformability and imaging cells from each fraction, we were
able to use deep learning to classify RBCs based on
deformability with a mean testing accuracy of 81%.
Furthermore, we used this approach to estimate the rigidity
scores (RS) of a RBC sample, which deviated by a mean of
10.4% from physical measurement using the microfluidic
ratchet device. We previously showed significant inter-donor
variability in RBC deformability,
9
which were successfully
captured by our deep learning model. To ensure our
inference of RBC deformability was robust to factors like well
location, lighting conditions, and presence of debris, we
introduced additional data variance by purposefully dividing
RBC specimens into multiple imaging wells and augmenting
cell images by rotation during database creation. The
generalizability of this method was investigated by using a
donor-specific trained model to evaluate the deep learning
derived deformability profile from unsorted donor RBCs
(Fig. 7). The RS obtained by this approach showed strong
agreement with microfluidic sorting, deviating by 1.0–12.6%
from the microfluidic measurement, which is comparable to
previously reported variability of microfluidic ratchet
measurement (13.8% from five independent measurements).
9
We used saliency maps to confirm that cellular features,
not imaging artifacts, were used by the convolutional neural
network for RBC classification. The saliency maps in Fig. 5
indicate that cell surface features, especially morphological
contours, are the main characteristics used for deformability
prediction. Other deep learning models have been used to
Fig. 7 Generalization of the deep learning network applied to unsorted cell images (light lines) compared to microfluidic-derived deformability
cumulative distributions (dark lines) for donor 2 (A), 3 (B), and 4 (C). The model generalizes well on the unsorted datasets, illustrated by similar
cumulative distribution plots and rigidity scores between the ground truth microfluidic results and the deep learning model tested on an unsorted
RBC sample.
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classify RBCs based on microscopy imaging, including those
related to malaria disease status
38
and the RBC storage
lesion.
37
Both malaria infection
58
and storage
9,67,68
are
associated with the loss of RBC deformability, suggesting that
the morphological features detected by these models also
correspond to deformability changes. Here, we confirm that
deep learning models can indeed infer cell deformability
from morphological features obtained by imaging.
Assessing RBC deformability using microscopy imaging
and deep learning has several important advantages over
physical measurement. First, although the throughput of
microfluidic devices used to measure RBC deformability is
higher than other methods, the throughput of this process
is limited by the high deformability of RBCs, which requires
the application of small and precisely controlled stress and
sufficient time to observe a response. In contrast, using
machine learning to infer RBC deformability allows for
evaluation of cells densely seeded within imaging wells at a
rate of ∼1500 cells imaged per minute. Second, a major
barrier to physical measurement of RBC deformability is
that these methods are often difficult, time consuming, and
require specialized equipment.
34,63,64
In contrast,
microscope systems are ubiquitous in both research and
clinical laboratories. Finally, while there are several methods
available for physical measurement of RBC deformability,
there are no accepted standards by which to compare
studies. Here, we use the microfluidic ratchet mechanism to
sort cells, which can be calibrated by sorting size-specific
microbeads to provide a measurement standard (Fig. S1†).
Therefore, the universality of microscope systems could
offer an approach to standardize RBC deformability
measurements in order to extend studies across multiple
centers. In summary, RBC assessment by machine learning
is more accessible, simpler to perform, and more
standardizable compared to physical RBC deformability
measurements.
To our knowledge, this work describes the first instance of
employing deep learning to predict RBC deformability. Deep
learning has been used previously to characterize other
cellular properties of RBCs. For example, Doan et al.
37
trained
a deep learning model to classify unlabeled images of stored
RBCs into seven morpho-types with 77% accuracy, which was
comparable to 83% agreement in manual classification by
experts. Other studies trained deep learning models to
identify RBCs from patients with malaria,
38–43
sickle cell
disease,
44–49
and thalassemia,
50–52
based on visually
identifiable changes in RBC morphology. Our application of
machine learning in RBC deformability measurement
deviates from these previous efforts because cellular features
corresponding to deformability are beyond human
perception. This result expands on our previous study using
deep learning to distinguish between cell lines that lack
human distinguishable features,
53
which further supports
our belief that imperceivable cellular parameters, such as
changes in biophysical or metabolic cell state, may be
detectable from cell images using deep learning.
Methods
RBC sample collection and preparation
This study was approved by the University of British
Columbia Clinical Research Ethics Board (UBC REB# H19-
01121) and the Canadian Blood Services Research Ethics
Board (CBS REB# 2019-029). Blood samples were collected
from donors following informed consent. Donors who self-
identified as healthy and were between the ages of 18–70
provided fresh RBCs in citrate tubes (n= 3), stored in an
Eppendorf tube (n= 1), or stored in blood bags (n=6)
(Table 3). Donors were diverse in terms of blood type and sex
(Table 3).
Blood sample components were separated by centrifuging
at 3900 rpm for 8 minutes at room temperature. Plasma
supernatant and leukocyte buffy coat were removed and
disposed. The RBC pellet was resuspended and washed three
times using Hanks balanced salt solution (HBSS, Gibco) with
0.2% Pluronic solution (F127, MilliporeSigma) by
centrifuging at 1800 rpm for 5 minutes. After all supernatant
and leukocytes are removed, the RBC pellet was diluted to
1% hematocrit in HBSS + 0.2% Pluronic for infusion into the
microfluidic device.
Microfluidic ratchet device manufacture
The manufacture of the microfluidic devices has been
described previously.
55,58
The master device mold was created
using photolithographic microfabrication and was used to
create a secondary master polyurethane mold fabricated out
of Smooth-Cast urethane resin (Smooth-Cast ONYX SLOW,
Smooth-On) as described here.
65
Single-use microfluidic
ratchet devices were molded from the secondary master
using PDMS silicone (Sylgard-184, Ellsworth Adhesives)
mixed at a 10 : 1 ratio with the PDMS curing agent (Sylgard-
184, Ellsworth Adhesives). The PDMS molded devices were
then cured for two hours at 65 °C. The cured PDMS devices
were removed from the molds and manually punched with
0.5 and 3.0 mm hole punches (Technical Innovations). A thin
PDMS silicone (RTV 615, Momentive Performance Materials
LLC) layer was manufactured to seal the device's
microstructures. This layer was produced by spin coating
uncured PDMS on a 100 mm silicon wafer at 1500 rpm for 1
minute, then was cured for 2 hours at 65 °C. The Sylgard-184
PDMS microstructure mold was bonded to the RTV 615 thin
PDMS layer using air plasma (Model PDC-001, Harrick
Plasma). Finally, the composite sealed microstructure mold
was then bonded to a 75 ×50 mm glass slide (Corning) using
air plasma.
Microfluidic device operation
The mechanism of using microscale funnel constrictions to
measure cell deformability and the operation of the
microfluidic device has been described and validated
previously.
7,9,10,16,31–33,54–57
The microfluidic ratchet sorting
device is operated via 4 pressurized fluidic inputs. A
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horizontal crossflow moves the sample towards the outlets,
while a vertical (relative to Fig. 2A–C) oscillating pressure
system squeezes cells through the tapered constrictions and
declogs others unable to pass through. The sorting matrix
of micropores has openings ranging from 1.5 μm to 7.5 μm
(Table 1). The resolution of these bins is limited by the
microfabrication process, whereby the resolution of the
mask is limited to 250 nm while our photolithography
wavelength is limited to 340 nm. Therefore, the smallest
feature resolution possible with this microfabrication
process is ∼250 nm. Before the RBC sample is infused, the
device is buffered with HBSS with 0.2% Pluronic-F127
solution through the horizontal crossflow inlet at high
pressure (300 mbar) for 15 minutes. Once the device is
buffered, 10 μL of HBSS with 0.2% Pluronic-F127 solution
is pipetted into each outlet (which are open to atmospheric
pressure) to improve ease of removal of each RBC
deformability sample after sorting. The RBC sample for
each donor is suspended at 1% hematocrit in HBSS with
0.2% Pluronic-F127 and then infused into the microfluidic
device at 40–45 mbar through the sample inlet. The sample
flows through the constriction matrix via the horizontal
crossflow pressure (55–60 mbar) and oscillatory pressure of
175 mbar upwards and 162 mbar downwards (relative to
Fig. 2B and C). The oscillation cycle of these pressures
occurs over 5 seconds: 4 seconds of upward pressure flow
for filtration, then 1 second of downward flow for
declogging. The sorting process throughput is approximately
600 cells per minute; the device is run for 60–90 minutes,
resulting in over 30 000 sorted cells. After the cells are
sorted through the constriction matrix, they proceed to one
of 12 distinct deformability outlets. The distribution of
sorted cells is determined by capturing images as the cells
exit the constriction matrix and counting the cells manually
using ImageJ.
66
The distribution can also be determined by
video analysis of the cells travelling through the constriction
matrix exit channels towards the outlets, or by removing the
cells in the outlets by pipetting for counting. Sorted RBCs
suspended in 10 μL of HBSS with 0.2% Pluronic-F127 from
each outlet are removed by pipetting and placed in a 96
well plate (VWR International, LLC) for imaging.
Microbead sorting validation
Microbead sorting validation was conducted to ensure device
manufacturing and sorting consistency between different
devices and users. To mimic deformable RBCs, 1.53 μm
polystyrene beads (Cat #17133, Polysciences Inc.) were
infused into the microfluidic device at 0.1% concentration in
HBSS with 0.2% Pluronic F127 and 0.2% TWEEN-20
(MilliporeSigma) to prevent bead aggregation. The bead
solution was run through the microfluidic device for 20
minutes, images were captured as the beads exited the matrix
sorting region, and the distribution was determined by
manually counting beads in ImageJ. Users 1 and 2 conducted
14 total tests using devices from five different master molds.
Intra- and inter-user microbead sorting was consistent (Fig.
S1†). The statistical analysis is found in the ESI.†
Image acquisition
After microfluidic sorting, sorted RBCs were removed from
the microfluidic device and transferred to a 96-well flat-
bottom plate (VWR International, LLC). Samples of sorted
cells from each outlet were divided evenly and placed into
two separate wells to provide cell images captured with
variations in well location distribution and automatically
determined imaging parameters (e.g. auto-exposure and auto-
focus) to produce robust datasets. Full image scans of each
well in 40×brightfield were acquired using a Nikon Ti-2E
inverted microscope and NIS Elements software. Illumination
for the brightfield images was implemented by using the
built-in Ti-2E LED. Gain, exposure, and vertical offset were
automatically determined by built-in NIS Elements functions
for consistency and to avoid user bias. Components of the
full image scan were 2424 ×2424 pixel BMP images with 24-
bit depth.
Segmentation and augmentation
Each full scan image was segmented using a custom
computer vision segmentation algorithm. Individual cells
were identified using a watershed algorithm and were
segmented into 60 ×60 pixel PNG images with 8-bit depth.
Segmented images with multiple or partial cells were
manually removed. Resultant single cell images from each
donor were split at an 80 : 20 ratio per class to create separate
training and testing datasets. After database splitting, images
were augmented by a random multiple of 90°rotation (0°,
90°, 180°, or 270°). This augmentation allowed for the
building of balanced training (10 000 images per outlet) and
testing (2000 images per outlet) datasets for each donor. In
addition, different lighting conditions were observed
depending on the location of the cell in the well. By
augmenting the cells by rotation this potential data
confounder is mitigated.
Convolution neural network model
A CNN, shown in Fig. 1F, was designed in Python 3.7 using
the Keras library in TensorFlow. The network accepts a
1-channel input of 60 ×60 pixels. The model begins with a
256-channel convolution layer with a kernel size of 7 and a
stride of 1. Next, the model uses a 2 ×2 max-pooling layer
with a stride of 2. The next layer is a 128-channel
convolutional layer with a kernel size of 5 ×5 and a stride of
1, followed by another max-pooling layer of size 2 ×2 with a
stride of 2. Next are two 64-channel convolutional layers in
series, each with kernel sizes of 3 and strides of 1. These
layers were followed by a max-pooling layer of size 2 ×2 with
a stride of 2. Then, the layer outputs were flattened into a
1-dimensional array for connection to the fully connected
layers. Each of the four convolutional layers were followed by
ReLU activation and batch normalization. Then, three 128-
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node fully connected dense layers, consisting of ReLU
activation and 20% dropout, were used by the model to learn
on the identified features from the earlier convolutional
layers. The network outputs two nodes, one per class, with a
SoftMax (normalized exponential) error function for
backpropagation.
Training environment
The segmentation and deep learning software were run on a
desktop PC operating Windows 10 Pro with an AMD Ryzen 7
5800X 8-core processor running at 3.80 GHz. The computer
used 64.0 GB DDR4 RAM running at 3200 MHz. The graphics
card used was a NVIDIA GeForce RTX 3070. Training and
testing were conducted in Python 3.7.11 utilizing the
TensorFlow 2.5.0 library.
Training
For each execution of the network, training occurred for 25 to
80 epochs with stochastic gradient descent optimization and
a learning rate between 0.0001 and 0.1. The appropriate
number of epochs and learning rate were determined
iteratively to find the best combination for training
convergence and validation accuracy for each donor/dataset
combination. Training concluded after there was no
improvement in the loss after the past five epochs. A batch
size of 32 and a categorical cross entropy loss function from
the Tensorflow Keras library (version 2.5.0) were used. The
error function used for backpropagation was the SoftMax
function. Additionally, the model was optimized for training
accuracy and was validated using five-fold cross validation.
Testing
To verify the accuracy on the five validation folds, testing
occurred on 2000 images per outlet from the separate testing
dataset. This dataset was split from the training set prior to
augmentation, ensuring images used for testing were
previously unseen by the network. In addition to deep
learning testing conducted on microfluidic sorted cells,
unsorted cell images from donor 2 (6307 images), 3 (9003
images), and 4 (2562 images) were also assessed.
Data availability statement
The data that support the findings of this study are available
on request from the corresponding author.
Funding statement
This work was supported by grants from the Canadian
Institutes of Health Research (322375, 362500, 414861),
Natural Sciences and Engineering Research Council of
Canada (538818-19, 2015-06541), MITACS (K. M. IT09621),
and the Canadian Blood Services Graduate Fellowship
Program (E. I.), which is funded by the federal government
(Health Canada) and the provincial and territorial ministries
of health. The views herein do not necessarily reflect the
views of Canadian Blood Services or the federal, provincial,
or territorial governments of Canada.
Ethics approval statement
This study was approved by the University of British
Columbia's Clinical Research Ethics Board (UBC REB# H19-
01121) and Canadian Blood Services Research Ethics Board
(CBS REB# 2019-029).
Author contributions
H. M. supervised the study. H. M., E. L., and S. P. D.
conceived the idea. E. L., E. I., and K. M. performed the
experimental work. E. L. and M. W. performed the
computational work. All authors wrote the manuscript.
Conflicts of interest
H. M. is listed as inventors on a patent related to this work.
Acknowledgements
We are grateful to Canadian Blood Services' blood donors
who made this research possible.
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