Human treelike tubular structure segmentation in medical images
Segmentation of treelike tubular structures in medical imaging is crucial for accurate diagnosis and treatment. Traditional methods often struggle with the complex morphology and inherent data variability of structures like blood vessels and lung branching. To tackle these challenges, this work pres...
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| Tipo de documento: | dissertação |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2023 |
| País: | Colombia |
| Recursos: | Universidad de los Andes |
| Repositório: | Séneca: repositorio Uniandes |
| Idioma: | inglês |
| OAI Identifier: | oai:repositorio.uniandes.edu.co:1992/73410 |
| Acesso em linha: | https://hdl.handle.net/1992/73410 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Segmentation Blood vessels Airways Nerves Computer vision Medical images CT MRI Ingeniería |
| Resumo: | Segmentation of treelike tubular structures in medical imaging is crucial for accurate diagnosis and treatment. Traditional methods often struggle with the complex morphology and inherent data variability of structures like blood vessels and lung branching. To tackle these challenges, this work presents three significant contributions. First, it introduces a comprehensive dataset aggregation, focusing on tubular structures, to challenge and benchmark existing segmentation algorithms. Second, an innovative evaluation framework is developed, surpassing traditional metrics by accurately assessing segmentation quality based on geometrical and topological characteristics of tubular structures. Lastly, the thesis proposes the Joint Brain-Vessel Segmentation (JoB-VS) framework, an end-to-end solution for segmenting brain vessels in TOF-MRA images, enhancing performance by forgoing additional preprocessing steps. These contributions collectively advance the field of medical image analysis, bridging the gap between technical segmentation techniques and their clinical application, thereby enhancing diagnostics and treatment planning in healthcare. |
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