Automatic tracking of vessel-like structures from a single starting point

A deep learning-based automated algorithm for labeling coronary arteries in computed tomography angiography images

OBJECTIVE

Using two three-dimensional U-Net architectures for myocardium structure extraction and a distance transformation algorithm specifically for the left circumflex artery, we have designed a fully automated algorithm for coronary artery labeling in coronary computed tomography angiography (CCTA) images.

METHODS

In this retrospective analysis, a cohort of 157 patients who had undergone coronary computed tomography angiography (CCTA) was included. An automated coronary artery labeling algorithm was developed using a distance transformation approach to delineate the anatomical segments along the centerlines extracted from the CCTA images. A total of 16 segments were successfully identified and labeled. The algorithm's outcomes were recorded and reviewed by three experts, and the performance of segment detection and labeling was assessed. Additionally, the level of agreement in manually labeled segments between two experts was quantified.

RESULTS

When comparing the labels generated by the experts with those produced by the algorithm, it was necessary to modify or eliminate 117 labels (5.4%) out of 2180 segments assigned by the algorithm. The overall accuracy for label presence was 96.2%, with an average overlap of 94.0% between the expert reference and algorithm-generated labels. Furthermore, the average agreement rate between the two experts stood at 95.0%.

CONCLUSIONS

Based on the labels of the clinical experts, the proposed deep learning algorithm exhibits high accuracy for automatic labeling. Therefore, our proposed method exhibits promising results for the automatic labeling of the coronary arteries and will alleviate the burden on radiologists in the near future.

Semi-automatic vessel detection for challenging cases of peripheral arterial disease

OBJECTIVES

Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Peripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifestations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data.

METHODS

We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically classifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization.

RESULTS

We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufficient quality for clinical application, with our current clinically established workflow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average specificity and overall accuracy of 99.9%.

CONCLUSIONS

Compared to the clinical workflow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.

Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis

BACKGROUND AND OBJECTIVE

Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.

MATERIALS AND METHODS

A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included the CCTA images of 100 patients which are trained using convolutional neural networks (CNN) in order to further identify various plaque classifications and coronary stenosis. The other 50 CAD patients acted as testing dataset that is evaluated by comparing the auto-reconstructed CCTA images with traditional CCTA images on the condition that DSA images are regarded as the reference method. Receiver operating characteristic (ROC) analysis was used for statistical analysis to compare CCTA-AI with DSA and traditional CCTA in the aspect of detecting coronary stenosis and plaque features.

RESULTS

AI significantly reduces time for post-processing and diagnosis comparing to the traditional methods. In identifying various degrees of coronary stenosis, the diagnostic accuracy of CCTA-AI is better than traditional CCTA (AUC_AI = 0.870, AUC_CCTA = 0.781, P < 0.001). In identifying ≥ 50% stenotic vessels, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of CCTA-AI and traditional method are 86% and 83%, 88% and 59%, 85% and 94%, 73% and 84%, 94% and 83%, respectively. In the aspect of identifying plaque classification, accuracy of CCTA-AI is moderate compared to traditional CCTA (AUC = 0.750, P < 0.001).

CONCLUSION

The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.

Segmentation of Cerebrovascular Anatomy from TOF-MRA Using Length-Strained Enhancement and Random Walker

Cerebrovascular rupture can cause a severe stroke. Three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) is a common method of obtaining vascular information. This work proposes a fully automated segmentation method for extracting the vascular anatomy from TOF-MRA. The steps of the method are as follows. First, the brain is extracted on the basis of regional growth and path planning. Next, the brain's highlighted connected area is explored to obtain seed point information, and the Hessian matrix is used to enhance the contrast of image. Finally, a random walker combined with seed points and enhanced images is used to complete vascular anatomy segmentation. The method is tested using 12 sets of data and compared with two traditional vascular segmentation methods. Results show that the described method obtains an average Dice coefficient of 90.68%, and better results were obtained in comparison with the traditional methods.

Computer aided detection of deep inferior epigastric perforators in computed tomography angiography scans

The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation requires preoperative imaging studies to evaluate the branches - the perforators - that irrigate the tissue that will be used to reconstruct the breast mound. These branches will support tissue viability after the microsurgical ligation of the inferior epigastric vessels to the receptor vessels in the thorax. Usually through a computed tomography angiography (CTA), each perforator is manually identified and characterized by the imaging team, who will subsequently draw a map for the identification of the best vascular support for the reconstruction. In the current work we propose a semi-automatic methodology that aims at reducing the time and subjectivity inherent to the manual annotation. In 21 CTAs from patients proposed for breast reconstruction with DIEAP flaps, the subcutaneous region of each perforator was extracted, by means of a tracking procedure, whereas the intramuscular portion was detected through a minimum cost approach. Both were subsequently compared with the radiologist manual annotation. Results showed that the semi-automatic procedure was able to correctly detect the course of the DIEAPs with a minimum error (average error of 0.64 and 0.50 mm regarding the extraction of subcutaneous and intramuscular paths, respectively), taking little time to do so. The objective methodology is a promising tool in the automatic detection of perforators in CTA and can contribute to spare human resources and reduce subjectivity in the aforementioned task.

Extrahepatic Clinical Application of Vessel Tracking Software and 3D Roadmapping Tools: Preliminary Experience

This article demonstrates the use of a representative commercially available automated vessel-tracking software originally intended for liver-only application (Vessel Assist Flight Plan for Liver; GE) in 4 patients. Treatment settings included embolization of small bowel hemorrhage source, treatment of renal cell carcinoma, management of symptomatic benign prostate hypertrophy, and detection with subsequent closure of a mesenteric pseudoaneurysm. All patients were treated successfully.

Automated identification and grading of coronary artery stenoses with X-ray angiography

BACKGROUND AND OBJECTIVE

X-ray coronary angiography (XCA) remains the gold standard imaging technique for the diagnosis and treatment of cardiovascular disease. Automatic detection and grading of coronary stenoses in XCA are challenging problems due to the complex overlap of different background structures with intensity inhomogeneities. We present a new computerized image based method to accurately identify and quantify the stenosis severity on XCA.

METHODS

A unified framework, consisting of Hessian-based vessel enhancement, level-set skeletonization, improved measure of match measurement, and local extremum identification, is developed to distinctly reveal the vessel structures and accurately determine the stenosis grades. The methodology was validated on 143 consecutive patients who underwent diagnostic XCA through both qualitative and quantitative evaluations.

RESULTS

The presented algorithm was tested on a set of 267 vessel segments annotated by two expert cardiologists. The experimental results show that the method can effectively localize and quantify the vessel stenoses, achieving average detection accuracy, sensitivity, specificity, and F-score of 93.93%, 91.03%, 93.83%, 89.18%, respectively.

CONCLUSIONS

A fully automatic coronary analysis method is devised for vessel stenosis detection and grading in XCA. The presented approach can potentially serve as a generalized framework to handle different image modalities.

3D Active Vessel Tracking Using an Elliptical Prior

In this paper, we propose a novel vessel tracking method, called active vessel tracking (AVT). The proposed method retains the major advantages that most 2D segmentation methods have demonstrated for 3D tracking while overcoming the drawbacks of previous 3D vessel tracking methods. Under the assumption that the vessel is cylindrical, thereby making its cross-section elliptical, the AVT finds a plane perpendicular to the vessel axis while tracking the vessel along its length. Also, We propose a method for vessel branch detection to automatically track complete vascular networks from a single starting point, whereas the previously proposed solutions have usually been limited in handling vessel bifurcations precisely on 3D or have required considerable user interaction. Our results show that the method is robust and accurate in both synthetic and clinical cases. In an experiment on synthetic data sets, the proposed method achieved a tracking accuracy of 96.1±0.5, detecting 99.1% of the branches. In an experiment on abdominal CTA data sets, it achieved a tracking accuracy of 98.4±0.5 for six target vessels, detecting 98.3% of the branches. These results show that the proposed method can outperform previous methods for vessel tracking.

Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging

BACKGROUND AND OBJECTIVE

Coronary artery segmentation is a fundamental step for a computer-aided diagnosis system to be developed to assist cardiothoracic radiologists in detecting coronary artery diseases. Manual delineation of the vasculature becomes tedious or even impossible with a large number of images acquired in the daily life clinic. A new computerized image-based segmentation method is presented for automatically extracting coronary arteries from angiography images.

METHODS

A combination of a multiscale-based adaptive Hessian-based enhancement method and a statistical region merging technique provides a simple and effective way to improve the complex vessel structures as well as thin vessel delineation which often missed by other segmentation methods. The methodology was validated on 100 patients who underwent diagnostic coronary angiography. The segmentation performance was assessed via both qualitative and quantitative evaluations.

RESULTS

Quantitative evaluation shows that our method is able to identify coronary artery trees with an accuracy of 93% and outperforms other segmentation methods in terms of two widely used segmentation metrics of mean absolute difference and dice similarity coefficient.

CONCLUSIONS

The comparison to the manual segmentations from three human observers suggests that the presented automated segmentation method is potential to be used in an image-based computerized analysis system for early detection of coronary artery disease.