Learning-Based Algorithms for Vessel Tracking: A Review

• URL: http://arxiv.org/abs/2012.08929v1
• Date: Wed, 16 Dec 2020 13:31:51 GMT
• Title: Learning-Based Algorithms for Vessel Tracking: A Review
• Authors: Dengqiang Jia, Xiahai Zhuang
• Abstract summary: Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods.
• Score: 12.18340575383456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.

Related papers

• RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection [6.671669971067487]
  This paper proposes a novel method for detecting retinal branching angles using a self-configured image processing technique. We offer an open-source annotation tool and a benchmark dataset comprising 40 images annotated with retinal branching angles.
  arXiv  Detail & Related papers  (2024-07-17T02:37:39Z)
• An automated framework for brain vessel centerline extraction from CTA images [28.173407996203153]
  We propose an automated framework for brain vessel centerline extraction from CTA images. The proposed framework outperforms state-of-the-art methods in terms of average symmetric centerline distance (ASCD) and overlap (OV) Subgroup analyses suggest that the proposed framework holds promise in clinical applications for stroke treatment.
  arXiv  Detail & Related papers  (2024-01-13T11:01:00Z)
• Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels [63.415444378608214]
  Our approach incorporates innovative techniques including progressive semi-supervised learning, adaptative training strategy, and boundary enhancement. Experimental results on 3DRA datasets demonstrate the superiority of our method in terms of mesh-based segmentation metrics.
  arXiv  Detail & Related papers  (2023-08-07T14:16:52Z)
• Overview of Deep Learning Methods for Retinal Vessel Segmentation [0.0]
  Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases. With the fast development of deep learning methods, more and more retinal vessel segmentation methods are implemented as deep neural networks.
  arXiv  Detail & Related papers  (2023-06-01T17:05:18Z)
• Deep Algorithm Unrolling for Biomedical Imaging [99.73317152134028]
  In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling. We trace the origin of algorithm unrolling and provide a comprehensive tutorial on how to unroll iterative algorithms into deep networks. We conclude the chapter by discussing open challenges, and suggesting future research directions.
  arXiv  Detail & Related papers  (2021-08-15T01:06:26Z)
• Recent advances and clinical applications of deep learning in medical image analysis [7.132678647070632]
  We reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images.
  arXiv  Detail & Related papers  (2021-05-27T18:05:12Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)
• Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imaging [3.2214522506924093]
  This study focuses on learning the causal relationship regarding the structure of the training data suitable for deep learning, to solve highly underdetermined inverse problems. We observe that a majority of the problems of solving underdetermined linear systems in medical imaging are highly non-linear. Furthermore, we analyze if a desired reconstruction map can be learnable from the training data and underdetermined system.
  arXiv  Detail & Related papers  (2020-01-06T07:52:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.