Title

Catheter Localization and Tracking using Convolutional Neural Networks with Generative Modeling

Alternative Title

생성 모델링을 사용한 컨볼루션 신경망을 이용한 카테터 위치 결정 및 추적

DGIST Authors

Ihsan Ullah ; Sang Hyun Park ; Sunghoon Im

Advisor

박상현

Co-Advisor(s)

Sunghoon Im

Issued Date

2023

Awarded Date

2023-02-01

Citation

Ihsan Ullah. (2023). Catheter Localization and Tracking using Convolutional Neural Networks with Generative Modeling. doi: 10.22677/THESIS.200000653296

Type

Thesis

Description

Catheter Segmentation, Catheter Synthesis, Domain Adaptation, deep convolutional neural network, Semantic Segmentation

Table Of Contents

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

URI

http://hdl.handle.net/20.500.11750/45675
http://dgist.dcollection.net/common/orgView/200000653296

DOI

10.22677/THESIS.200000653296

Degree

Doctor

Department

Department of Robotics and Mechatronics Engineering

Publisher

DGIST

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