A review of 3D vessel lumen segmentation techniques: Models,features and extraction schemes |
| |
| Authors: | David Lesage Elsa D Angelini Isabelle Bloch Gareth Funka-Lea |
| |
| Institution: | 1. Institute of Electronics, Lodz University of Technology, Lodz, Poland;2. Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University, Jena, Germany;3. Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University, Jena, Germany;4. Abbe School of Photonics, Friedrich Schiller University, Jena, Germany;5. Center of Medical Optics and Photonics, Friedrich Schiller University, Jena, Germany;1. Beijing University of Chemical Technology, Beijing 100029, People''s Republic of China;2. Department of Hepatobiliary Surgery, Chinese PLA 309th Hospital, Beijing 100091, People''s Republic of China;3. Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, People''s Republic of China;1. School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea;2. School of Computer Science and Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Korea;3. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 110-799, Korea |
| |
| Abstract: | Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies.Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task.Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
