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Deep learning system in contrast-enhanced
MR for Microvascular invasion
characterization of hepatocellular carcinoma
김 준모1, 이 민우1,2, 신 수용3
1 성균관대학교 삼성융합의과학원 융합의과학과 2 성균관대학교 의과대학, 삼성서울병원 영상의학과 3 성균관대학교 삼성융합의과학원 디지털헬스학과
Introduction
■ Microvascular invasion(MVI) of hepatocellular carcinoma (HCC)
– Major risk factor for tumor recurrence
– Difficult to diagnose before initiating treatment
■ Gd-EOB-DTPA- enhanced MRI
– Accurate tumor staging
– Enhance survival outcome
– Biomarkers predicting tumor aggressiveness
– No solid data regarding usefulness of deep learning system
■ Predicting presence of MVI
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Introduction
■ Purpose
– To classify presence of MVI using fully automated deep learning system
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Method
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Patients
N = 549
MVI Positive
N = 163
MVI Negative
N = 292
Patients included
N = 455
Capsule
N = 21
Non MVI
N = 271
Capsule and
Peritumoral
N = 41
Peritumoral
N = 122
Exclusion (N=94)
Motion artifact N=31
Image missing N=44
Low image quality N=18
Postoperative image N=1
■ From Jan 2010 to Dec 2014
■ Gd-EOB-DTPA- enhanced MRI
■ 455 pts (mean age,56 ; range, 25-90)
Method: Overview
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Our proposed Deep Learning system for MVI of HCC
Method: Preprocessing ■ Resizing to 320x320 grayscale image from variety of image size
(400x400 ~ 256x256)
■ Selecting lesion ROIs (Region of Interests)
■ Generating Mask
■ Augmenting Data
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Method: Preprocessing ■ Data augmentation
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RANDOM
ROTATION
RANDOM SHIFT RANDOM ZOOM
IN, OUT
Increased number of images
1. MVI Negative - Original (N=3,900) -> (N=27,300)
2. MVI Positive - Original (N=2,495) -> (N=17,465)
Method CNN Architecture
Using Keras (Deep Learning library)
Fine tuned Convolutional neural network (CNN)
Visualizing Class-Specific Units using Class Activation mapping (CAM)
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Result
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Result
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Showing the area of interest of the trained model (CAM Method)
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No.28 | Normal 5.74% | mVI_Neg 94.26% | mVI_Pos 0.00%
Peritumoral
Result
No.49 | Normal 6.08% | mVI_Neg 14.83% | mVI_Pos 79.09%
Peritumoral
False Positive
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Result Decision support system for MVI of HCC
Limitations
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■ Single center study
– Only PHILIPS MR system data
– Absence of external validation with other MR manufacturers
■ Based on hepatobiliary phase alone
– Lack of arterial phase images due to frequent image degradation by motion artifact
■ Absence of comparison with human readers
Summary
■ Performance for MVI detection using deep learning system
– Precision: 99%
– Sensitivity 83%
■ Visualizing MVI Positive and Negative of maps in EOB MR Images
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Additional materials
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Method
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■ Dataset details
■ Hepatobiliary phase
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precision = 𝑇𝑃
𝑇𝑃 + 𝐹𝑃
Sensitivity = 𝑇𝑃
𝑇𝑃+𝐹𝑁
𝐹1 = 2 ∗ 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑟𝑒𝑐𝑎𝑙𝑙/(𝑝𝑟𝑒𝑐𝑖𝑠𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙)
Result
Decision support system for MVI of HCC
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