This document describes a study using deep learning to detect cerebral microbleeds (CMBs) from susceptibility weighted imaging (SWI). It found that using both SWI and phase images as inputs to a neural network led to better performance than SWI alone. The best model achieved 95.8% sensitivity and 70.9% precision, similar to human experts. This outperformed previous studies and demonstrated the potential of deep learning for medical image analysis tasks.

1. Cerebral Microbleed Detection
using Susceptibility Weighted Imaging
and Deep Learning
Saifeng Liu
May, 2019

2. Cerebral Microbleed Detection
• Cerebral microbleed (CMB) detection is important in studying dementia,
stroke and traumatic brain injury (TBI)
• Susceptibility Weighted Imaging (SWI) provides exquisite sensitivity to the
presence of CMB
• Manual detection of CMBs
 Time consuming
 Prone to errors (false positive or missed CMB)
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3. • Conventional machine learning algorithms
 Require feature engineering (shape, intensity etc.)
 Variation in the shape and intensity of CMBs leads to low sensitivity or low specificity
• Current deep learning based algorithms
 Utilize SWI (magnitude) images only -> difficulties in separating calcifications and CMBs
• Phase images are valuable for separting CMBs and CMB mimics (e.g. calcifications, veins)
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Magnitude Phase
: CMB
: Calcification
Cerebral Microbleed Detection

4. CMB Detection using Multi-channel 3D MRI Data
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1. CMB
Candidate
Detection
using SWI
2. False
Positive
Removal
using Phase
and SWI
: CMB
: False Positive
*
*3D-FRST: 3D Fast Radial Symmetry Transform

5. Why 3D-FRST for CMB Candidate Detection
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B0
CMBs are close to
spheres on SWI
images, due to the
shape of the induced
field variation.
TE=10ms TE=2.5ms
Magnitude
Phase

6. Data Information and Pre-processing
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Hemodialysis TBI Stroke Normal Control Total
Training 72 (756)a 80 (522) 2 (2) 0 154 (1280)
Validation 15 (160) 8 (33) 2 (0) 0 25 (193)
Test 13 (95) 9 (14) 9 (59) 10 (0) 41 (168)
Data
Splitting
B0 (T) TE (ms) TR (ms) FA (o) In-plane
Resolution (mm2)
Slice Thickness
(mm)
1.5 or 3 40@1.5T;
20@3T
49 or 50 @ 1.5T;
27 to 34 @ 3T
12 or 15 0.5x0.5 to
0.54x1.07
1.2 to 2.65
Imaging
Parameters
aNumber of Cases (Number of CMBs)
Bias-field
Correction
(for magnitude)
Registration
(Magnitude with
MNI-152 template)
Generate
SWI&QSMb
Interpolation
(to 0.5mm
isotropic)
Intensity
Normalization
(to [-1, 1])
Pre-processing
bQSM: quantitative susceptibility mapping

7. Selection of 3D-FRST Threshold
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th=0.1
Training and Validation
Selection of the 3D-FRST
threshold using train and
validation data: a trade-off
between sensitivity and FPavg.
Test Data Performance
Sensitivity: 99.4%
FPavg: 276.8 per case
th: 0.09
th=0.09 th=0.08

8. Overall Performance on Test Data
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The best combination was
phase and SWI (PS model).
The PS model achieved
similar performance to the
most experienced human
rater (Rater3).
Rater1: SWI data processor
Rater2: Radiologist
Rater3: Experienced SWI
data processor (8+ years)

9. Contribution of Individual Channels,
Effects of Model Averaging and Input Scale
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• QSM > SWI > phase > magnitude
• Simple averaging of multiple models (dashed lines) with the same type of input did not help.
• Smaller input scale reduced the performance.

10. Separating CMBs from Calcification
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Magnitude Phase SWI QSM
3D-FRST SWI SWI with Candidates SWI Model Prediction Phase+SWI Model Prediction
: calcification

11. Performance of PS Model in Each Case and Error Analysis
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False positives caused by the asymmetric
prominent cortical veins (APCV) in a stroke case.
Missed lesion due to small size in a hemodialysis case. Missed classified damaged veins in a TBI case.

12. Comparison with Earlier Studies
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Models Input Number of parameters Sensitivity (%) Precision (%) FP per CMB
Chen et al.1
SWI ~200,000 94.7 72.0 0.4
Dou et al.2
SWI ~1,380,000 93.2 44.3 1.2
SWI Model SWI 71,650 91.7 52.0 0.8
Phase+SWI Model Phase + SWI 72,082 95.8 70.9 0.4
1Chen et al. Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network. J Digit Imaging. 2018.
2Dou et al. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE Trans Med Imaging. 2016;35(5):1182–95

13. Discussion
• Limited sample size of different categories
 variation in the performance for different types of disease
• Lack of comprehensive comparison with earlier algorithms
 used the SWI model as a reference
 compared with human raters
• The raw phase images were not available
 limited quality of QSM -> the best combination of input channels is phase
and SWI
• Use of conventional 3D-FRST for candidate selection:
 fully convolutional network may further improve time-efficiency
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14. Conclusions
• The use of both SWI and phase images leads to significant improvement in the
model performance.
• Preprocessing is important for utilizing 3D multi-contrast MRI data.
• The best PS model outperformed earlier studies and achieved similar
performance to the most experienced human rater.
• There is great potential of applying deep learning techniques to medical
imaging.
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15. • A paper based on this study has been published on NeuroImage:
• Liu S, Utriainen D, Chai C, Chen Y, Wang L, Sethi SK, Xia S, Haacke EM. Cerebral
microbleed detection using Susceptibility Weighted Imaging and deep
learning. NeuroImage. 2019 May 20.
• https://doi.org/10.1016/j.neuroimage.2019.05.046
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