Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network

Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation is performed in two stages. First, a bounding box around the LV is detected using a combination of three convolutional neural networks (CNNs). Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN. The study included CCTA scans of sixty patients, fifty scans were used to train the CNNs for the LV localization, five scans were used to train LV segmentation and the remaining five scans were used for testing the method. Automatic segmentation resulted in the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1 mm. The results demonstrate that automatic segmentation of the LV in CCTA scans using voxel classification with convolutional neural networks is feasible.Comment: This work has been published as: Zreik, M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., I\v{s}gum, I. (2016, April). Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 40-43). IEE

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of $0.81 \pm 0.02$ on the artery-level, and $0.87 \pm 0.02$ on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.Comment: This work has been accepted to IEEE TMI for publicatio

Molecular testing for the clinical diagnosis of fibrolamellar carcinoma.

Fibrolamellar carcinoma has a distinctive morphology and immunophenotype, including cytokeratin 7 and CD68 co-expression. Despite the distinct findings, accurate diagnosis of fibrolamellar carcinoma continues to be a challenge. Recently, fibrolamellar carcinomas were found to harbor a characteristic somatic gene fusion, DNAJB1-PRKACA. A break-apart fluorescence in situ hybridization (FISH) assay was designed to detect this fusion event and to examine its diagnostic performance in a large, multicenter, multinational study. Cases initially classified as fibrolamellar carcinoma based on histological features were reviewed from 124 patients. Upon central review, 104 of the 124 cases were classified histologically as typical of fibrolamellar carcinoma, 12 cases as 'possible fibrolamellar carcinoma' and 8 cases as 'unlikely to be fibrolamellar carcinoma'. PRKACA FISH was positive for rearrangement in 102 of 103 (99%) typical fibrolamellar carcinomas, 9 of 12 'possible fibrolamellar carcinomas' and 0 of 8 cases 'unlikely to be fibrolamellar carcinomas'. Within the morphologically typical group of fibrolamellar carcinomas, two tumors with unusual FISH patterns were also identified. Both cases had the fusion gene DNAJB1-PRKACA, but one also had amplification of the fusion gene and one had heterozygous deletion of the normal PRKACA locus. In addition, 88 conventional hepatocellular carcinomas were evaluated with PRKACA FISH and all were negative. These findings demonstrate that FISH for the PRKACA rearrangement is a clinically useful tool to confirm the diagnosis of fibrolamellar carcinoma, with high sensitivity and specificity. A diagnosis of fibrolamellar carcinoma is more accurate when based on morphology plus confirmatory testing than when based on morphology alone

The application of a real-time rapid-prototyping environment for the behavioral rehabilitation of a lost function in rats

Abstract-In this paper we propose a Rapid Prototyping Environment (RPE) for real-time biosignal analysis including ECG, EEG, ECoG and EMG of humans and animals requiring a very precise time resolution. Based on the previous RPE which was mainly designed for developing Brain Computer Interfaces (BCI), the present solution offers tools for data preprocessing, analysis and visualization even in the case of high sampling rates and furthermore tools for precise cognitive stimulation. One application of the system, the analysis of multi-unit activity measured from the brain of a rat is presented to prove the efficiency of the proposed environment. The experimental setup was used to design and implement a biomimetic, biohybrid model for demonstrating the recovery of a learning function lost with age. Throughout the paper we discuss the components of the setup, the software structure and the online visualization. At the end we present results of a real-time experiment in which the model of the brain learned to react to the acquired signals