Deep Open Snake Tracker for Vessel Tracing
- URL: http://arxiv.org/abs/2107.09049v1
- Date: Mon, 19 Jul 2021 17:59:31 GMT
- Title: Deep Open Snake Tracker for Vessel Tracing
- Authors: Li Chen, Wenjin Liu, Niranjan Balu, Mahmud Mossa-Basha, Thomas S.
Hatsukami, Jenq-Neng Hwang, Chun Yuan
- Abstract summary: Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health.
Existing algorithms have been developed but there are certain persistent problems such as incomplete or inaccurate vessel tracing.
We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images.
- Score: 32.97987423431042
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Vessel tracing by modeling vascular structures in 3D medical images with
centerlines and radii can provide useful information for vascular health.
Existing algorithms have been developed but there are certain persistent
problems such as incomplete or inaccurate vessel tracing, especially in
complicated vascular beds like the intracranial arteries. We propose here a
deep learning based open curve active contour model (DOST) to trace vessels in
3D images. Initial curves were proposed from a centerline segmentation neural
network. Then data-driven machine knowledge was used to predict the stretching
direction and vessel radius of the initial curve, while the active contour
model (as human knowledge) maintained smoothness and intensity fitness of
curves. Finally, considering the nonloop topology of most vasculatures,
individually traced vessels were connected into a tree topology by applying a
minimum spanning tree algorithm on a global connection graph. We evaluated DOST
on a Time-of-Flight (TOF) MRA intracranial artery dataset and demonstrated its
superior performance over existing segmentation-based and tracking-based vessel
tracing methods. In addition, DOST showed strong adaptability on different
imaging modalities (CTA, MR T1 SPACE) and vascular beds (coronary arteries).
Related papers
- Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario [0.8749675983608172]
This model intends to provide a dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms.
In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, and we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.
arXiv Detail & Related papers (2024-11-04T18:08:24Z) - Fitting tree model with CNN and geodesics to track vesselsand
application to Ultrasound Localization Microscopy data [5.1082516810678396]
We build a model to carry tracking on Ultrasound Localization Microscopy (ULM) data.
We also test our framework on synthetic and eye fundus data.
Results show that scarcity of well annotated ULM data is an obstacle to localization of vascular landmarks.
arXiv Detail & Related papers (2023-11-13T09:25:03Z) - SIRE: scale-invariant, rotation-equivariant estimation of artery
orientations using graph neural networks [2.439909645714735]
We present SIRE: a scale-invariant, rotation-equivariant estimator for local vessel orientation.
SIRE can be trained with arbitrarily oriented vessels with varying radii to generalise to vessels with a wide range of calibres and tortuosity.
We embed SIRE in a centerline tracker which accurately tracks AAAs, regardless of the data SIRE is trained with.
arXiv Detail & Related papers (2023-11-09T14:32:57Z) - AiAReSeg: Catheter Detection and Segmentation in Interventional
Ultrasound using Transformers [75.20925220246689]
endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
arXiv Detail & Related papers (2023-09-25T19:34:12Z) - SE(3) symmetry lets graph neural networks learn arterial velocity
estimation from small datasets [3.861633648502351]
Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning.
Velocity fields are typically obtained from patient-specific 3D artery models via computational fluid dynamics (CFD)
We propose graph neural networks (GNN) as an efficient black-box surrogate method to estimate 3D velocity fields.
arXiv Detail & Related papers (2023-02-17T09:42:38Z) - An Algorithm for the Labeling and Interactive Visualization of the
Cerebrovascular System of Ischemic Strokes [59.116811751334225]
VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans.
We extend the labeling mechanism for the cerebral arteries to identify occluded vessels.
We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
arXiv Detail & Related papers (2022-04-26T14:20:26Z) - 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) - 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) - Tattoo tomography: Freehand 3D photoacoustic image reconstruction with
an optical pattern [49.240017254888336]
Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties.
A current drawback is the limited field-of-view provided by the conventionally applied 2D probes.
We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
arXiv Detail & Related papers (2020-11-10T09:27:56Z) - 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) - 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.