Few Scanline Deep Learning Network for Ultrasound Image Segmentation
- Title
- Few Scanline Deep Learning Network for Ultrasound Image Segmentation
- Alternative Title
- 적은 수의 스캔라인을 이용한 초음파 이미지 분할 딥러닝 네트워크
- Author(s)
- Minji Kang
- DGIST Authors
- Minji Kang ; Jae Youn Hwang ; Jin Ho Chang
- Advisor
- 황재윤
- Co-Advisor(s)
- Jin Ho Chang
- Issued Date
- 2023
- Awarded Date
- 2023-02-01
- 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
4. CONCLUSION
REFERENCES 46
요 약 문 49
- URI
-
http://hdl.handle.net/20.500.11750/45745
http://dgist.dcollection.net/common/orgView/200000657134
- Degree
- Master
- Publisher
- DGIST
- Related Researcher
-
-
Hwang, Jae Youn
- Research Interests Multimodal Imaging; High-Frequency Ultrasound Microbeam; Ultrasound Imaging and Analysis; 스마트 헬스케어; Biomedical optical system
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- Files in This Item:
-
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- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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