Morphology Edge Attention Network and Optimal Geometric Matching Connection model for vascular segmentation

• URL: http://arxiv.org/abs/2306.01808v2
• Date: Wed, 13 Dec 2023 09:19:13 GMT
• Title: Morphology Edge Attention Network and Optimal Geometric Matching Connection model for vascular segmentation
• Authors: Yuntao Zhu, Yuxuan Qiao, Xiaoping Yang
• Abstract summary: We propose a novel Morphology Edge Attention Network (MEA-Net) for the segmentation of vessel-like structures. We also present an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel segments. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks.
• Score: 3.6368619769561668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There are many unsolved problems in vascular image segmentation, including vascular structural connectivity, scarce branches and missing small vessels. Obtaining vessels that preserve their correct topological structures is currently a crucial research issue, as it provides an overall view of one vascular system. In order to preserve the topology and accuracy of vessel segmentation, we proposed a novel Morphology Edge Attention Network (MEA-Net) for the segmentation of vessel-like structures, and an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel segments. The MEA-Net has an edge attention module that improves the segmentation of edges and small objects by morphology operation extracting boundary voxels on multi-scale. The OGMC model uses the concept of curve touching from differential geometry to filter out fragmented vessel endpoints, and then employs minimal surfaces to determine the optimal connection order between blood vessels. Finally, we calculate the geodesic to repair missing vessels under a given Riemannian metric. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks, both effectively reducing vessel broken and increasing vessel branch richness, yielding blood vessels with a more precise topological structure.

Related papers

• Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging [33.23991248643144]
  We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality. HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel.
  arXiv  Detail & Related papers  (2023-11-22T11:15:38Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images [6.027522272446452]
  The study of the retinal vasculature is a fundamental stage in the screening and diagnosis of many diseases. We propose a novel approach for the simultaneous segmentation and classification of the retinal A/V from eye fundus images.
  arXiv  Detail & Related papers  (2022-09-20T09:54:01Z)
• Modeling and hexahedral meshing of cerebral arterial networks from centerlines [0.0]
  Centerline-based representation is widely used to model large vascular networks with small vessels. We propose an automatic method to generate a structured hexahedral mesh suitable for CFD directly from centerlines. We demonstrate the efficiency of our method by entirely meshing a dataset of 60 cerebral vascular networks.
  arXiv  Detail & Related papers  (2022-01-20T16:30:17Z)
• Edge-competing Pathological Liver Vessel Segmentation with Limited Labels [61.38846803229023]
  There is no algorithm as yet tailored for the MVI detection from pathological images. This paper collects the first pathological liver image dataset containing 522 whole slide images with labels of vessels, MVI, and carcinoma grades. We propose an Edge-competing Vessel Network (EVS-Net) which contains a segmentation network and two edge segmentation discriminators.
  arXiv  Detail & Related papers  (2021-08-01T07:28:32Z)
• Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation [58.45470500617549]
  We propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images.
  arXiv  Detail & Related papers  (2021-05-31T06:55:43Z)
• Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation [5.926887379656135]
  We propose a new deep convolutional neural network (CNN) which divides vessel segmentation into two separate objectives. Specifically, we consider the overall accurate vessel segmentation and tiny vessel segmentation as two individual objectives. To improve the individual objectives, we propose: (a) a vessel weight map based auxiliary loss for enhancing tiny vessel connectivity, and (b) an enhanced encoder-decoder architecture for improved localization.
  arXiv  Detail & Related papers  (2021-04-17T14:17:09Z)
• Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction [57.74609918453932]
  Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. We propose a hybrid representation learning approach to address this challenge.
  arXiv  Detail & Related papers  (2020-12-14T05:22:49Z)
• Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation [53.187152856583396]
  We propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features.
  arXiv  Detail & Related papers  (2020-10-09T08:22:54Z)
• ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model [41.444917622855606]
  We release a dedicated OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level. Secondly, we propose a novel Split-based Coarse-to-Fine vessel segmentation network (SCF-Net), with the ability to detect thick and thin vessels separately. In the SCF-Net, a split-based coarse segmentation (SCS) module is first introduced to produce a preliminary confidence map of vessels, and a split-based refinement (SRN) module is then used to optimize the shape/contour of
  arXiv  Detail & Related papers  (2020-07-10T06:54:19Z)
• clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation [57.20783326661043]
  We introduce a novel similarity measure termed centerlineDice (short clDice) We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D)
  arXiv  Detail & Related papers  (2020-03-16T16:27:49Z)

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.