This document describes a deep learning approach for automatic brain tumor segmentation on MRI scans. A convolutional neural network is trained on over 3 million patches from 220 glioma patients to classify voxels into tumor subclasses like enhancing tumor, necrosis, and edema. The trained model segments a new MRI scan with over 90% accuracy in under 20 minutes, significantly faster than manual segmentation by experts. This efficient and accurate segmentation technique could maximize treatment effectiveness while maintaining patient privacy in the cloud-based system.

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Technology Co.
Andrew Tsuei
Technical Director
Automatic Brain Tumor Segmentation
on Multi-Modal MRI with Deep Neural
Networks
2017/9

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Gliomas are one of the most common types of primary brain
tumors.
Glioblastoma multiforme (malignant brain tumor) cells have
irregular shapes with fingers that can spread into the brain, which
causes Brain Tumor Segmentation in MRI relatively difficult.
Brain tumor segmentation seeks to separate healthy tissue from
tumorous regions such as the enhancing tumor, necrosis and
surrounding edema. This is an essential step in diagnosis and
treatment planning, both of which need to take place as soon as
possible in order to maximize the likelihood of successful
treatment.
By utilizing Deep Neuro Networks and GPU-accelerated cloud
computer power for Brain Tumor Segmentation in MRI, we
achieve:
Benefits
Processing large
amount of MRI
data with
competitive
performance
1More
consistent
quality
(not only depending on
experts’ experiences)
2Accurate
result for
diagnosis and
treatment
planning 3

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Multi-Modal MRI
(3T MRI Scans, 155 slices per sequence)
3
The Service
To get a satisfactory manual segmentation a
radiologist must spend several hours on more than
600 images determining which voxels belong to
which class.
Automatic Segmentation
With Deep Neural Networks
and Computer Vision
techniques, to efficiently
and accurately perform
segmentation
Segmentation
Model
(Deep Learning:
Convolutional Neural
Network based model)
T1 T2 T1C FLAIR
One Single Slice
(with 4 pulse sequence)
Segmentation
For one single slice

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Approach to train the Deep Neural Networks
Patch Extraction
Training
Healthy
Enhancing Tumor
Edema
Necrosis / Non-Enhancing
Tumor
Classification
• Manually annotated by clinical experts, and expert neuroradiologists have radiologically assessed
• Trained with 3 Million patches sampled from 220 High-Grade Glioma Patients
T1
T2
T1C
FLAIR
Label
3 Million patches

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Trained ModelPixel by pixel Post Processing
Auto Segmentation
MRI Images Visualization
Upload
Auto Segmentation with Trained Model
Analysis &
Visualization
GPU accelerated VM
Client
T1 T2 T1C FLAIR
Client

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Low Cost, pay per usage
6
Deployment Scenario 1
Model Training
Task Manager
Browser
T1 T2 T1C FLAIR
MRI Images
WEB Server API Server
Segmentation
Task Manager
Training Task Queue
Segmentation
Task Queue Task Workers
Task Workers
Azure
Image Storage Database
Cloud-based SaaS Architecture
Model Training
Task Manager

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Hybrid Architecture
7
Deployment Scenario 2
• Performing
segmentation in the on-
premises environment
with trained model
• Sync with most updated
trained model with the
cloud
• Upload NIFTI images or
extracted patches
(features) to cloud for
model training, so that
NO patient’s privacy
info are kept in the
cloud.
Browser
T1 T2 T1C FLAIR
MRI Images
WEB
Server
API
Server
Segmentation
Task Manager
Segmentation
Task Queue
Task Workers
On-Premise
Image
Storage
Database
Browser
T1 T2 T1C FLAIR
MRI Images
(for Training)
WEB
Server
API
Server
Model Training
Task Manager
Training Task
Queue
Task Workers
Azure
Image
Storage
Database

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Current Result
Environment
Azure NC-12
CPU 12 Cores (E5-2690v3)
GPU 2 NVIDIA K80 GPU
(1 Physical Card)
Memory 112GB
Average Time
for Processing
15S/slice
(pre-process time not included)
(155 slices / 4 modalities):
< 20 mins
Ground Truth
By Expert
Auto. Segmentation
By AI Model
Necrosis Enhancing
Tumor
Edema
process whole brain for one patient
in 20 minutes with 90% accuracy
…………………………………………………….
～90.8%
Accuracy
( Correct / Total ) * 100%

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No risk of leaking patient privacy and
confidentiality
Maximizing the likelihood of
successful treatment
Efficient and Accurate Consistent
Conclusion
We aim to provide a machine learning based computer-aided diagnosis service that is:
Brain tumor segmentation is an essential step in
diagnosis and treatment planning, both of which
need to take place as soon as possible in order to
maximize the likelihood of successful treatment.
No any patient privacy information is needed
during both training and prediction processes.
Professional experiences can be preserved and
learned from, so as to provide consistent result
Fewer time a radiologist has to spend to get a
satisfactory segmentation

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© 2017 WiAdvance Technology Co. All rights reserved.