859 research outputs found
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images
is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We
propose an algorithm that extracts coronary artery centerlines in CCTA using a
convolutional neural network (CNN).
A 3D dilated CNN is trained to predict the most likely direction and radius
of an artery at any given point in a CCTA image based on a local image patch.
Starting from a single seed point placed manually or automatically anywhere in
a coronary artery, a tracker follows the vessel centerline in two directions
using the predictions of the CNN. Tracking is terminated when no direction can
be identified with high certainty.
The CNN was trained using 32 manually annotated centerlines in a training set
consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery
Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08
challenge showed that extracted centerlines had an average overlap of 93.7%
with 96 manually annotated reference centerlines. Extracted centerline points
were highly accurate, with an average distance of 0.21 mm to reference
centerline points. In a second test set consisting of 50 CCTA scans, 5,448
markers in the coronary arteries were used as seed points to extract single
centerlines. This showed strong correspondence between extracted centerlines
and manually placed markers. In a third test set containing 36 CCTA scans,
fully automatic seeding and centerline extraction led to extraction of on
average 92% of clinically relevant coronary artery segments.
The proposed method is able to accurately and efficiently determine the
direction and radius of coronary arteries. The method can be trained with
limited training data, and once trained allows fast automatic or interactive
extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Automatic rib labeling and anatomical centerline extraction are common
prerequisites for various clinical applications. Prior studies either use
in-house datasets that are inaccessible to communities, or focus on rib
segmentation that neglects the clinical significance of rib labeling. To
address these issues, we extend our prior dataset (RibSeg) on the binary rib
segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT
scans (15,466 individual ribs in total) and annotations manually inspected by
experts for rib labeling and anatomical centerline extraction. Based on the
RibSeg v2, we develop a pipeline including deep learning-based methods for rib
labeling, and a skeletonization-based method for centerline extraction. To
improve computational efficiency, we propose a sparse point cloud
representation of CT scans and compare it with standard dense voxel grids.
Moreover, we design and analyze evaluation metrics to address the key
challenges of each task. Our dataset, code, and model are available online to
facilitate open research at https://github.com/M3DV/RibSegComment: 10 pages, 6 figures, journa
Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms
Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: 1) a method is described to create a consensus centerline with multiple observers, 2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, 3) a database containing thirty-two cardiac CTA datasets with corresponding reference standard is described and made available, and 4) thirteen coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms
Coronary Artery Tracking in 3D Cardiac CT Images Using Local Morphological Reconstruction Operators
Automatic segmentation and tracking of the coronary artery tree from Cardiac Multislice-CT images is an important goal to improve the diagnosis and treatment of coronary artery disease. This paper presents a semi-automatic algorithm (one input point per vessel) based on morphological grayscale local reconstructions in 3D images devoted to the extraction of the coronary artery tree. The algorithm has been evaluated in the framework of the Coronary Artery Tracking Challenge 2008 [1], obtaining consistent results in overlapping measurements (a mean of 70% of the vessel well tracked). Poor results in accuracy measurements suggest that future work should refine the centerline extraction. The algorithm can be efficiently implemented and its general strategy can be easily extrapolated to a completely automated centerline extraction or to a user interactive vessel extractio
3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation
This paper proposes a simple and efficient method for the reconstruction and
extraction of geometric parameters from 3D tubular objects. Our method
constructs an image that accumulates surface normal information, then peaks
within this image are located by tracking. Finally, the positions of these are
optimized to lie precisely on the tubular shape centerline. This method is very
versatile, and is able to process various input data types like full or partial
mesh acquired from 3D laser scans, 3D height map or discrete volumetric images.
The proposed algorithm is simple to implement, contains few parameters and can
be computed in linear time with respect to the number of surface faces. Since
the extracted tube centerline is accurate, we are able to decompose the tube
into rectilinear parts and torus-like parts. This is done with a new linear
time 3D torus detection algorithm, which follows the same principle of a
previous work on 2D arc circle recognition. Detailed experiments show the
versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing,
Sep 2015, Genova, Italy. 201
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