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Highly Accurate Brain Stroke Diagnostic System and
Generative Lesion Model
Junghwan Cho, Ph.D.CAIDE Systems, Inc.
Deep Learning R&D Team
� Established in September, 2016 at 110 Canal st. Lowell, MA 01852, USACEO & Founder: Jacob Kyewook Lee
Employees : 6 Contact: caideinfo@caidesystems.com
� Our Mission Save human lives by developing Cognitive Artificial Intelligence Disease Detection Systems. Provide protection of human life and equal access to health care and treatment through artificial intelligence technology.
� Our Goals Eliminating human errors and reducing delayed diagnosisDeveloping the most reliable AI system for analyzing images (ultra sound, MRI, CT and X-ray), electronic medical records, and genome data.
� Available PositionLooking for Talented Research Scientist or Engineer
With CAIDE, Better and Healthier Life!
http://www.caidesystems.com
Outlines
§CAIDE Diagnostic System for Brain Stroke§Stroke Classification/Stroke Lesion Segmentation §Stroke Lesion Generative Network§Demo- CAIDE m: Studio BSR
With CAIDE, Better and Healthier Life!
§ Brain attack/accident
§ Up to 2 million brain cells die every minute.
§ About 795,000 people suffer from stroke every year in US.
§ More than 137,000 people (17% of all strokes) die from
the stroke, with a cost of approximately $76.3 billion.
*Source image from http://www.stroke.org/understand-stroke/what-stroke/stroke-facts
Brain Stroke
Ischemic Stroke (Blood Blockage) Hemorrhagic Stroke (Bleeding)
CT Findings on Intracranial Hemorrhage Types
Epidural (EDH) Subdural (SDH)
Subarachnoid (SAH)Intraparenchymal (IPH) Intraventricular (IVH)
IPH+IVH
CAIDE Diagnostic SystemDICOM Files--->Gray Scale (on Window Level/Width)
VS
Hemorrhagic(bleeding) No Bleeding
False Negative
NO
NOPositive?(Bleeding)
CT Images
Preprocessing
CNN1- Classifier(Default Window)
CNN2- Classifier(Stroke Window)
Stroke LesionDelineation (FCN) Positive?
YES
YES
END
Cascaded CNN ClassifiersModel 1
Model 2
Sensitivity (Recall) vs Specificity
Cascaded CT window for increasing sensitivity while preserving specificity
>>
Sensitivity Specificity
False Positives Rate (1- Specificity)
0 0.05 0.1 0.15 0.2 0.250.85
0.9
0.95
1ROC for Classification
True
Pos
itive
s R
ate
(Sen
sitiv
ity)
ROC for Classification
(0.991, 0.961)= (Specificity, Sensitivity)
(0.953, 0983)
Thp>=0.5
Thp>=0.2
Default Window vs Stroke Window Setting
50/100 (WL/WW) 40/40
Ground Truth
• Narrow window width (high-contrast)• Increase detection of subtle abnormalities
50/100
Default Brain Window Stroke Window
Turner, P. J., and G. Holdsworth. "CT stroke window settings: an unfortunate misleading misnomer?." The British journal of radiology 84, no. 1008 (2011): 1061-1066.
Training for Cascaded CNN Classifier (Bleeding or not)• Total data sets- 5,647 patients (3,000 no bleeding vs 2,647 bleeding)
o2D axial CT images with 512x512 size • 5-fold cross validation• Trained cascaded CNN model
oTwo different training solvers: Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM)
oScratch vs fined tuned using pre-trained model• Hardware computer: NVIDIA DGX-1 with 8 Tesla V100
Evaluation- Classification- (top 1- accuracy) – after 15 epoch
1 2 3 490
92
94
96
98
100
SGD-Scratch
SGD-Fine-tuned
ADAM-Scratch
ADAM-Fine-tuned
("#. "" ± &. '%)
("*. *+ ± &. ,%)
("-. ". ± &. .%) ("'. +' ± &. +%)%
Evaluation – Cascade CT Window Increasing Sensitivity
1 20
50
100
150
200
250
300
350
50/100(WL/WW)
50/100+40/40 (Cascaded)
# of
False
Neg
ativ
e CT
Imag
es
%
1 2 3 495
96
97
98
99
100
50/100+40/40
50/100 50/100+40/40 (Cascaded)
Specificity
Sensitivity
50/100(WL/WW)
Outlines
§CAIDE Diagnostic System for Brain Stroke§Stroke Lesion Segmentation§Stroke Lesion Generative Network§Demo- CAIDE m: Studio BSR
Encoder-Decoder Architecture- for sematic image segmentation
• Encoder
§ Feature extraction (Convolution)
§ Dimensional reduction (Pooling)
§ VGG 16 or ResNet
• SegNet, U-Net, and Fully Convolutional Network (FCN)
• Decoder
§ High resolution from low resolution
§ Unpooling/up-sampling with
transposed convolution (deconvolution)
Source image from "Segnet”, IEEE transactions on pattern analysis and machine intelligence 39, no. 12 (2017): 2481-2495.
Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
14
Fully Convolutional Network (FCN) for Stroke Lesion Segmentation
Pool4 prediction
2x upsampled
SUM
FCN-8s
Pool3 prediction
2x upsampled
SUM
8x upsampled
Soft
max
1 2 3 4 5
VGG16 Network
FCN Training• Total data sets- 2,647 patients (corresponding 33,391 well labeled images)• 5-fold cross validation• Fully convolutional network with ADAM solver using pre-trained model• NVIDA DGX-1 (8 V100 GPU)
1 2 3 4 50
2000
4000
6000
8000
10000
12000
14000
IPH IVH EDH SDH SAH
Histogram of Hemorrhagic Stroke Type
IVH DCIVH=.86
IPH, SAH DCIPH=.89, DCSAH=.77
Segm
ente
d Re
sults
by
FCN
8s
afte
r 50
Epoc
h Tr
aini
ng
SAH, SDH DCSAH=. 84
EDH, SDH DCEDH=.89, DCSDH=.54
SDH: False Negative
SAH: False Positive
Segm
ente
d Re
sults
by
FCN
8s
afte
r 50
Epoc
h Tr
aini
ng
0 1 2 3 4 5 650
60
70
80
90
100
0 1 2 3 4 5 650
60
70
80
90
100
Performance Evaluation
IPH IVH EDH SDH SAH
% %
IPH IVH EDH SDH SAH
#FP: Number of Pixels Falsely Positive Segmented
#FP>300
#FP>200
#FP>100
Precision Recall, Sensitivity
DC>5%
DC>25%
DC>50%
Precision=TP/(TP+FP) Recall=TP/(TP+FN)
DC: Dice Coefficient
Outlines
§CAIDE Diagnostic System for Brain Stroke§Stroke Classification/Stroke Lesion Segmentation §Stroke Lesion Generative Network§Demo- CAIDE m: Studio BSR
Generative Adversarial Networks (GANs)• Two networks competing against each other in a zero sum game
Source Image from https://twitter.com/ch402/status/793911806494261248
Source Image from https://www.slideshare.net/ckmarkohchang/generative-adversarial-networks
(z)
(x)
The discriminator (D):Distinguish real data from fake created by the generator
The generator (G):Learn distribution of the data from random noise, in an attempt to fool the discriminator
Image to Image Translation- for Generating Stoke Lesion Images
Source image from, Phillip, et al. "Image-to-image translation with conditional adversarial networks." arXiv preprint (2017)
l Apply to map stoke lesion labels to corresponding lesion image.l Stoke lesion masks (segmented regions) - conditional input images to the
Generator (G) as well as Discriminator (D)
Stoke Lesion Labels
Lesion Generated Image
Target (Stroke Lesion)
GAN for Generating Stoke Lesion Images
Training Pix2Pix-Tensorflow
l Trained conditional GAN below conditionsq Total data set : 2,647 patients (corresponding 33,391 well labeled images)
: 80% training, 20% testing
q Learning parameters: Learning rate =0.0002, L1 weight=100, and GAN weight=1.0
q About 16 hour up to 200 epoch on NVIDIA Tesla V100 (1 GPU)
SDH
SAH
SAH
Input Target Output Input Target Output
Examples of Generated Fake CT Image after 200 Epoch Training
EDH
SAH
SAH, EDH
SAH
IVH, IPH
SAH, IVH
SAH, IVH, IPH
Input Target OutputInput Target Output
IPH, SAH
SAH, IVH
Evaluationl In general, evaluating GANs is difficult
q Loss function makes it harder during trainingq FCN /Inception scores and Amazon Mechanical Turk (AMT by human)
l FCN scores : fake/generated images inferred by FCNl Clarity- threshold blurriness (variance of Laplacian)l Second discriminator- choose more realistic images
Dice: .981 Dice: .976 Dice: .981 Dice: .978
10 epochs 50 epochs 100 epochs 200 epochs
IPH
FCN scores (DICE) vs Image Quality
RecallDICE (FCN Scores)
Evaluation – Recall, DICE l Evaluated by varying:
q Percentage of training data (based on patient number): 2.5, 10, 50, and 100%q Number of epochs : 10, 50, 100, and 200 epoch
Evaluation- Data Augmentation
Original(Real Data)
Original+Augment
Original+2xAugment
Original+3xAugment
CAIDE m: Studio BSR (Demo), Booth #726
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