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Automatic Brain Tumor Segmentation on Multi-Modal MRI with Deep Neural Networks

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Method and Architecture for Automatic Brain Tumor Segmentation on Multi-Modal MRI with Deep Neural Networks

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Automatic Brain Tumor Segmentation on Multi-Modal MRI with Deep Neural Networks

  1. 1. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. © 2017 WiAdvance Technology Co. All rights reserved. Technology Co. Andrew Tsuei Technical Director Automatic Brain Tumor Segmentation on Multi-Modal MRI with Deep Neural Networks 2017/9
  2. 2. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. 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
  3. 3. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. 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
  4. 4. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved.4 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
  5. 5. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved.5 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
  6. 6. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. 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
  7. 7. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. 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
  8. 8. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved.8 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%
  9. 9. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. 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
  10. 10. Technology Co.© 2017 WiAdvance Technology Co. All rights reserved. © 2017 WiAdvance Technology Co. All rights reserved.

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