WEB OF SCIENCE
SCOPUS
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 황재윤 | - |
| dc.contributor.author | Minji Kang | - |
| dc.date.accessioned | 2023-03-22T19:57:16Z | - |
| dc.date.available | 2025-02-28T06:00:31Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/45745 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000657134 | - |
| dc.description | Ultrasound, Wearable System, Segmentation, 초음파, 웨어러블 시스템, 분할 | - |
| dc.description.tableofcontents | 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 |
- |
| dc.format.extent | 62 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.title | Few Scanline Deep Learning Network for Ultrasound Image Segmentation | - |
| dc.title.alternative | 적은 수의 스캔라인을 이용한 초음파 이미지 분할 딥러닝 네트워크 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/THESIS.200000657134 | - |
| dc.description.degree | Master | - |
| dc.contributor.department | Department of Electrical Engineering and Computer Science | - |
| dc.identifier.bibliographicCitation | Minji Kang. (2023). Few Scanline Deep Learning Network for Ultrasound Image Segmentation. doi: 10.22677/THESIS.200000657134 | - |
| dc.contributor.coadvisor | Jin Ho Chang | - |
| dc.date.awarded | 2023-02-01 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.IM 강38 202302 | - |
| dc.date.accepted | 2023-03-21 | - |
| dc.contributor.alternativeDepartment | 전기전자컴퓨터공학과 | - |
| dc.subject.keyword | Ultrasound | - |
| dc.subject.keyword | Wearable System | - |
| dc.subject.keyword | Segmentation | - |
| dc.subject.keyword | 초음파 | - |
| dc.subject.keyword | 웨어러블 시스템 | - |
| dc.subject.keyword | 분할 | - |
| dc.contributor.affiliatedAuthor | Minji Kang | - |
| dc.contributor.affiliatedAuthor | Jae Youn Hwang | - |
| dc.contributor.affiliatedAuthor | Jin Ho Chang | - |
| dc.contributor.alternativeName | 강민지 | - |
| dc.contributor.alternativeName | Jae Youn Hwang | - |
| dc.contributor.alternativeName | 장진호 | - |
