Minji Kang. (2023). Few Scanline Deep Learning Network for Ultrasound Image Segmentation. doi: 10.22677/THESIS.200000657134
Type
Thesis
Description
Ultrasound, Wearable System, Segmentation, 초음파, 웨어러블 시스템, 분할
Table Of Contents
1. INTRODUCTION 1.1 Motivation 1 1.2 Background 3 1.2.1 The physics of ultrasound 3 1.2.2 Ultrasound imaging 5 1.2.3 Ultrasound wearable system 6 1.2.4 Deep learning technology 7 1.2.5 Generative Adversarial Network (GAN) 8 1.2.6 Segmentation model 10 1.3 Preliminary Study 13 1.3.1 Correlation between bladder volume and posture 13 1.3.2 Estimation of bladder signal in ultrasound A-mode 15
2. METHODS AND MATERIALS 2.1 Description of the Proposed Bladder Wearable System 17 2.2 Data Acquisition 19 2.2.1 Clinical data acquisition with commercial ultrasound equipment 19 2.2.2 Phantom data acquisition with a single-element transducer 22 2.3 Description of the Bladder Estimation Algorithm 26 2.3.1 Preparation of a paired image dataset 26 2.3.2 Deep learning experiment setup 28 2.3.2.1 GAN (Image-to-image translation) 29 2.3.2.2 Segmentation model (U-Net) 29 2.4 Quantitative Evaluation of Algorithm 30
3. RESULTS AND DISCUSSIONS 3.1 Generated a Larger Number of Scanlines Image 31 3.2 Segmented Bladder Ultrasound Image 34 3.3 Single-element Transducer Experiment Result 41
