Precise Lumen Segmentation in Coronary Computed Tomography Angiography

Lugauer, Felix; Zheng, Yefeng; Hornegger, Joachim; Kelm, B. Michael

doi:10.1007/978-3-319-13972-2_13

Cited by 18 publications

(25 citation statements)

References 12 publications

(20 reference statements)

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“…Although the focus of this paper is not vessel segmentation, it is worthwhile to mention that the current best Dice coefficient in the lumen segmentation challenge is 0.76 (Lugauer et al, 2014), which means that the 0.71 and 0.66 obtained by applying a simple thresholding after enhancement with FST and RPD respectively on one dataset is encouraging.…”

Section: Discussionmentioning

confidence: 92%

Gradient-based enhancement of tubular structures in medical images

Moreno

Smedby

2015

Medical Image Analysis

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Vesselness filters aim at enhancing tubular structures in medical images. The most popular vesselness filters are based on eigenanalyses of the Hessian matrix computed at different scales. However, Hessian-based methods have well-known limitations, most of them related to the use of second order derivatives. In this paper, we propose an alternative strategy in which ring-like patterns are sought in the local orientation distribution of the gradient. The method takes advantage of symmetry properties of ring-like patterns in the spherical harmonics domain. For bright vessels, gradients not pointing towards the center are filtered out from every local neighborhood in a first step. The opposite criterion is used for dark vessels. Afterwards, structuredness, evenness and uniformness measurements are computed from the power spectrum in spherical harmonics of both the original and the half-zeroed orientation distribution of the gradient. Finally, the features are combined into a single vesselness measurement. Alternatively, a structure tensor that is suitable for vesselness can be estimated before the analysis in spherical harmonics. The two proposed methods are called Ring Pattern Detector (RPD) and Filtered Structure Tensor (FST) respectively. Experimental results with computed tomography angiography data show that the proposed filters perform better compared to the state-of-the-art.

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Section: Discussionmentioning

confidence: 92%

Gradient-based enhancement of tubular structures in medical images

Moreno

Smedby

2015

Medical Image Analysis

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“…Image values were clipped at 0 and 1000 HU to normalize for surrounding low-density tissue, air and hyperdense calcifications. We observed that this circumvents the need for explicit calcium removal steps such as proposed in [10]. Networks were trained end-to-end and parameters were optimized using the Adam optimizer with a learning rate of 0.001.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Methods for stenosis detection [5] and blood flow simulation [12] typically require highly personalized coronary lumen surface meshes with sub-voxel accuracy. Because manual segmentation of the full coronary artery tree in a CCTA image would hardly be feasible, such meshes are typically extracted using automatic or semi-automatic methods [8,10,2]. Deep learning-based segmentation could further improve such methods [9], but widely adopted voxel-based segmentation methods do not meet the requirements of down-stream applications, i.e.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

Wolterink

Leiner

Išgum

2019

Graph Learning in Medical Imaging

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Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Surface meshes enclosing the full coronary artery tree were automatically extracted. A quantitative evaluation on 78 coronary artery segments showed that these meshes corresponded closely to reference annotations, with a Dice similarity coefficient of 0.75/0.73, a mean surface distance of 0.25/0.28 mm, and a Hausdorff distance of 1.53/1.86 mm in healthy/diseased vessel segments. The results showed that inclusion of mesh information in a GCN improves segmentation overlap and accuracy over a baseline model without interaction on the mesh. The results indicate that GCNs allow efficient extraction of coronary artery surface meshes and that the use of GCNs leads to regular and more accurate meshes.

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“…In some cases, identification of culprit lesions was additionally based on ECG abnormalities if these indicated a bad perfusion of a specific part of the heart muscle. In total, the data contained 345 lesions, which were annotated by defining their start and end centerline point and segmenting their inner and outer vessel wall using a fully automatic approach [9]. For all data sets, automatic centerline extraction was performed as described in [17].…”

Section: Datamentioning

confidence: 99%

Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics

Denzinger

Wels

Ravikumar

et al. 2019

Lecture Notes in Computer Science

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Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity-and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which -in contrast to our methods -requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79-0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.86, the deep learning approach 0.84 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events.

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Precise Lumen Segmentation in Coronary Computed Tomography Angiography

Cited by 18 publications

References 12 publications

Gradient-based enhancement of tubular structures in medical images

Gradient-based enhancement of tubular structures in medical images

Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics

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