Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 박상현 | - |
| dc.contributor.author | Ihsan Ullah | - |
| dc.date.accessioned | 2023-03-22T19:56:19Z | - |
| dc.date.available | 2023-03-22T19:56:19Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/45675 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000653296 | - |
| dc.description | Catheter Segmentation, Catheter Synthesis, Domain Adaptation, deep convolutional neural network, Semantic Segmentation | - |
| dc.description.tableofcontents | 1. Motivation, Research Problem and Contributions 1 1 Motivation 1 2 Main Contributions 2 3 Thesis Outline 4 4 Publications 5 2. Catheter Tip Tracking in Camera Sequences 7 1 Introduction 7 2 Related Work 9 3 Detect and Segment to Track 11 3.1 Detection Network 12 3.2 Segmentation Network 14 4 Experiments and Results 14 4.1 Dataset 14 4.2 Evaluation Settings 15 4.3 Quantitative Results 17 4.4 Qualitative Results 20 5 Discussion 21 5.1 Effectiveness of the proposed methods 21 5.2 Effect of data augmentation 21 6 Chapter Summary 22 3. Camera Catheter Translation to X-ray Catheter Sequences 23 1 Introduction 23 2 Related Work 25 2.1 Learning based methods for segmentation and detection 26 2.2 Image translation in medical imaging 26 3 Synthesize and Segment 27 3.1 Synthesize: GAN based X-ray translation 27 3.2 Segment: From synthesis to segmentation 29 4 Experiments 30 4.1 Datasets 30 4.2 Experimental Setup 31 4.3 Quantitative Results 32 4.4 Qualitative Results 34 5 Discussion 38 6 Chapter Summary 39 4. Domain Adaptive Segmentation: Generated X-ray Catheter to Real X-ray Catheter 41 1 Introduction 41 2 Related Work 43 2.1 Video Semantic Segmentation 43 2.2 Domain Adaptation for Semantic Segmentation 44 3 Learning Domain Adaptation for Semantic Segmentation 45 3.1 Problem Definition 45 3.2 Overview 45 3.3 Video Segmentation with PCM 47 4 Catheter Dataset Results 49 4.1 Catheter Dataset Experimental Setting 49 4.2 Comparative Analysis on Catheter Dataset 51 5 Cityscapes Dataset Results 52 5.1 Cityscapes Experimental Setting 52 5.2 Comparison with State-of-the-Art Methods 53 6 Discussion 57 6.1 PCM Mask Generation 57 6.2 Inconsistent Labels 58 7 Chapter Summary 58 5. Concluding Remarks and Future Work 60 6. Acknowledgement 62 References 63 |
- |
| dc.format.extent | 77 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.title | Catheter Localization and Tracking using Convolutional Neural Networks with Generative Modeling | - |
| dc.title.alternative | 생성 모델링을 사용한 컨볼루션 신경망을 이용한 카테터 위치 결정 및 추적 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/THESIS.200000653296 | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | Department of Robotics and Mechatronics Engineering | - |
| dc.contributor.coadvisor | Sunghoon Im | - |
| dc.date.awarded | 2023-02-01 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.RD IH25 202302 | - |
| dc.date.accepted | 2023-03-21 | - |
| dc.contributor.alternativeDepartment | 로봇및기계전자공학과 | - |
| dc.subject.keyword | Catheter Segmentation | - |
| dc.subject.keyword | Catheter Synthesis | - |
| dc.subject.keyword | Domain Adaptation | - |
| dc.subject.keyword | deep convolutional neural network | - |
| dc.subject.keyword | Semantic Segmentation | - |
| dc.contributor.affiliatedAuthor | Ihsan Ullah | - |
| dc.contributor.affiliatedAuthor | Sang Hyun Park | - |
| dc.contributor.affiliatedAuthor | Sunghoon Im | - |
| dc.contributor.alternativeName | Ihsan Ullah | - |
| dc.contributor.alternativeName | Sang Hyun Park | - |
| dc.contributor.alternativeName | 임성훈 | - |
| dc.rights.embargoReleaseDate | 2025-02-28 | - |
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