DGIST Scholar: Enhancement of Perivascular Spaces using 3D Convolutional Neural Network

Department of Robotics and Mechatronics Engineering Theses Master

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Enhancement of Perivascular Spaces using 3D Convolutional Neural Network

Title: Enhancement of Perivascular Spaces using 3D Convolutional Neural Network

Alternative Title: 3차원 합성곱 신경망을 이용한 혈관영상 화질 향상

Author(s): Euijin Jung

DGIST Authors: Park, Sanghyun ; Jung, Euijin ; Kim, Jaeil

Advisor: 박상현

Co-Advisor(s): Jaeil Kim

Issued Date: 2019

Awarded Date: 2019-02

Type: Thesis

Table Of Contents: Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

I. INTRODUCTION 1

II. Related Works 3
1 Spatial Domain Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Transform Domain Approaches . . . . . . . . . . . . . . . . . . . . . . . 3
3 Learning Based Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 4
4 Medical Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . 4

III. METHODS 6
1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 SRCNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 VDSR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 Dense Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5 Densely Connected Dense Network . . . . . . . . . . . . . . . . . . . . . 9

IV. RESULTS 10
1 Data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Evaluation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Qualitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Discussion for comparison networks . . . . . . . . . . . . . . . . . . . . . 15
6 Discussion for network depth . . . . . . . . . . . . . . . . . . . . . . . . . 18

V. CONCLUSION 20

References 21