4,347 research outputs found
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from
over-complete graphs, can have many varied applications. In this work, we
extract trees or collection of sub-trees from image data by, first deriving a
graph-based representation of the volumetric data and then, posing the tree
extraction as a graph refinement task. We present two methods to perform graph
refinement. First, we use mean-field approximation (MFA) to approximate the
posterior density over the subgraphs from which the optimal subgraph of
interest can be estimated. Mean field networks (MFNs) are used for inference
based on the interpretation that iterations of MFA can be seen as feed-forward
operations in a neural network. This allows us to learn the model parameters
using gradient descent. Second, we present a supervised learning approach using
graph neural networks (GNNs) which can be seen as generalisations of MFNs.
Subgraphs are obtained by training a GNN-based graph refinement model to
directly predict edge probabilities. We discuss connections between the two
classes of methods and compare them for the task of extracting airways from 3D,
low-dose, chest CT data. We show that both the MFN and GNN models show
significant improvement when compared to one baseline method, that is similar
to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based
airway segmentation model, in detecting more branches with fewer false
positives.Comment: Accepted for publication at Medical Image Analysis. 14 page
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
In this work, we adapt a method based on multiple hypothesis tracking (MHT)
that has been shown to give state-of-the-art vessel segmentation results in
interactive settings, for the purpose of extracting trees. Regularly spaced
tubular templates are fit to image data forming local hypotheses. These local
hypotheses are used to construct the MHT tree, which is then traversed to make
segmentation decisions. However, some critical parameters in this method are
scale-dependent and have an adverse effect when tracking structures of varying
dimensions. We propose to use statistical ranking of local hypotheses in
constructing the MHT tree, which yields a probabilistic interpretation of
scores across scales and helps alleviate the scale-dependence of MHT
parameters. This enables our method to track trees starting from a single seed
point. Our method is evaluated on chest CT data to extract airway trees and
coronary arteries. In both cases, we show that our method performs
significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical
Physics and Practic
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
We present tree extraction in 3D images as a graph refinement task, of
obtaining a subgraph from an over-complete input graph. To this end, we
formulate an approximate Bayesian inference framework on undirected graphs
using mean field approximation (MFA). Mean field networks are used for
inference based on the interpretation that iterations of MFA can be seen as
feed-forward operations in a neural network. This allows us to learn the model
parameters from training data using back-propagation algorithm. We demonstrate
usefulness of the model to extract airway trees from 3D chest CT data. We first
obtain probability images using a voxel classifier that distinguishes airways
from background and use Bayesian smoothing to model individual airway branches.
This yields us joint Gaussian density estimates of position, orientation and
scale as node features of the input graph. Performance of the method is
compared with two methods: the first uses probability images from a trained
voxel classifier with region growing, which is similar to one of the best
performing methods at EXACT'09 airway challenge, and the second method is based
on Bayesian smoothing on these probability images. Using centerline distance as
error measure the presented method shows significant improvement compared to
these two methods.Comment: 10 pages. Preprin
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Open international challenges are becoming the de facto standard for
assessing computer vision and image analysis algorithms. In recent years, new
methods have extended the reach of pulmonary airway segmentation that is closer
to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation,
limited effort has been directed to quantitative comparison of newly emerged
algorithms driven by the maturity of deep learning based approaches and
clinical drive for resolving finer details of distal airways for early
intervention of pulmonary diseases. Thus far, public annotated datasets are
extremely limited, hindering the development of data-driven methods and
detailed performance evaluation of new algorithms. To provide a benchmark for
the medical imaging community, we organized the Multi-site, Multi-domain Airway
Tree Modeling (ATM'22), which was held as an official challenge event during
the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed
pulmonary airway annotation, including 500 CT scans (300 for training, 50 for
validation, and 150 for testing). The dataset was collected from different
sites and it further included a portion of noisy COVID-19 CTs with ground-glass
opacity and consolidation. Twenty-three teams participated in the entire phase
of the challenge and the algorithms for the top ten teams are reviewed in this
paper. Quantitative and qualitative results revealed that deep learning models
embedded with the topological continuity enhancement achieved superior
performance in general. ATM'22 challenge holds as an open-call design, the
training data and the gold standard evaluation are available upon successful
registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/.
Submitte
Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation
Detailed pulmonary airway segmentation is a clinically important task for
endobronchial intervention and treatment of peripheral located lung cancer
lesions. Convolutional Neural Networks (CNNs) are promising tools for medical
image analysis but have been performing poorly for cases when existing a
significant imbalanced feature distribution, which is true for the airway data
as the trachea and principal bronchi dominate most of the voxels whereas the
lobar bronchi and distal segmental bronchi occupy a small proportion. In this
paper, we propose a Differentiable Topology-Preserved Distance Transform
(DTPDT) framework to improve the performance of airway segmentation. A
Topology-Preserved Surrogate (TPS) learning strategy is first proposed to
balance the training progress within-class distribution. Furthermore, a
Convolutional Distance Transform (CDT) is designed to identify the breakage
phenomenon with superior sensitivity and minimize the variation of the distance
map between the predictionand ground-truth. The proposed method is validated
with the publically available reference airway segmentation datasets. The
detected rate of branch and length on public EXACT'09 and BAS datasets are
82.1%/79.6% and 96.5%/91.5% respectively, demonstrating the reliability and
efficiency of the method in terms of improving the topology completeness of the
segmentation performance while maintaining the overall topology accuracy.Comment: 10 page
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