Automated axial right ventricle to left ventricle diameter ratio computation in computed tomography pulmonary angiography

Automated medical image analysis requires methods to localize anatomic structures in the presence of normal interpatient variability, pathology, and the different protocols used to acquire images for different clinical settings. Recent advances have improved object detection in the context of natural images, but they have not been adapted to the 3D context of medical images. In this paper we present a 2.5D object detector designed to locate, without any user interaction, the left and right heart ventricles in Computed Tomography Pulmonary Angiography (CTPA) images. A 2D object detector is trained to find ventricles on axial slices. Those detections are automatically clustered according to their size and position. The cluster with highest score, representing the 3D location of the ventricle, is then selected. The proposed method is validated in 403 CTPA studies obtained in patients with clinically suspected pulmonary embolism. Both ventricles are properly detected in 94.7% of the cases. The proposed method is very generic and can be easily adapted to detect other structures in medical images

Implementation and performance of automated software to compute the RV/LV diameter ratio from CT pulmonary angiography images

Objective: The aim of this study was to prospectively test the performance and potential for clinical integration of software that automatically calculates the right-to-left ventricular (RV/LV) diameter ratio from computed tomography pulmonary angiography images. Methods: Using 115 computed tomography pulmonary angiography images that were positive for acute pulmonary embolism, we prospectively evaluated RV/LV ratio measurements that were obtained as follows: (1) completely manual measurement (reference standard), (2) completely automated measurement using the software, and (3 and 4) using a customized software interface that allowed 2 independent radiologists to manually adjust the automatically positioned calipers. Results: Automated measurements underestimated (P < 0.001) the reference standard (1.09 [0.25] vs1.03 [0.35]). With manual correction of the automatically positioned calipers, the mean ratio became closer to the reference standard (1.06 [0.29] by read 1 and 1.07 [0.30] by read 2), and the correlation improved (r = 0.675 to 0.872 and 0.887). The mean time required for manual adjustment (37 [20] seconds) was significantly less than the time required to perform measurements entirely manually (100 [23] seconds). Conclusions: Automated CT RV/LV diameter ratio software shows promise for integration into the clinical workflow for patients with acute pulmonary embolism

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

Automated Axial Right Ventricle to Left Ventricle Diameter Ratio Computation in Computed Tomography Pulmonary Angiography

Background and Purpose Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE. Materials and Methods The CAD system was designed specifically for RV/LV measurements. The system was tested in 198 consecutive CTPA patients with acute PE. Its accuracy was evaluated using reference standard RV/LV radiologist measurements and its prognostic value was established for 30-day PE-specific mortality and a composite outcome of 30-day PE-specific mortality or the need for intensive therapies. The study was Institutional Review Board (IRB) approved and HIPAA compliant. Results The CAD system analyzed correctly 92.4% (183/198) of CTPA studies. The mean difference between automated and manually computed axial RV/LV ratios was 0.03±0.22. The correlation between the RV/LV diameter ratio obtained by the CAD system and that obtained by the radiologist was high (r=0.81). Compared to the radiologist, the CAD system equally achieved high accuracy for the composite outcome, with areas under the receiver operating characteristic curves of 0.75 vs. 0.78. Similar results were found for 30-days PE-specific mortality, with areas under the curve of 0.72 vs. 0.75. Conclusions An automated CAD system for determining the CT derived RV/LV diameter ratio in patients with acute PE has high accuracy when compared to manual measurements and similar prognostic significance for two clinical outcomes.Madrid-MIT M+Vision Consortiu

Technical note: Semiautomated targeted postmortem computed tomography angiography of the pulmonary arteries using a robotic system

INTRODUCTION To better depict vascular lesions on postmortem computed tomography (PMCT), whole-body postmortem computed tomography angiography (PMCTA) can be used in forensic diagnostics. Targeted angiography, in which only a specific vessel is filled with contrast agent, might help in cases of traumatic changes that render whole-body PMCTA impossible. Moreover, in targeted PMCTA, the contrast agent does not affect the haptics of any other organs. In this article, we describe automated, CT-guided targeted angiography of the pulmonary artery (PA) using the Virtobot system. MATERIAL AND METHODS Our study group consisted of 8 deceased persons (3 males, 5 females). We first performed an unenhanced CT scan and used the data obtained to plan the needle trajectories with the Virtobot planning software. Then, the needle was fully automatically placed by the Virtobot system. Subsequently, 50 ml of contrast agent was injected manually, and the CT scan was repeated (targeted PMCTA). RESULTS AND DISCUSSION We tested a new method for performing semiautomated targeted postmortem angiography of the PAs using a robotic needle placement system (Virtobot). In 6 out of our 8 cases, the injection of contrast agent in the PA was successful. In five of the six successful cases, there was reflux of contrast agent to some extent, but the reflux did not affect the readout. In general, the procedure was easy to plan based on a PMCT data set, and the pulmonary trunk was easy to reach with a robotic needle placement system

Quantitative planar and volumetric cardiac measurements using 64 mdct and 3t mri vs. Standard 2d and m-mode echocardiography: does anesthetic protocol matter?

Cross‐sectional imaging of the heart utilizing computed tomography and magnetic resonance imaging (MRI) has been shown to be superior for the evaluation of cardiac morphology and systolic function in humans compared to echocardiography. The purpose of this prospective study was to test the effects of two different anesthetic protocols on cardiac measurements in 10 healthy beagle dogs using 64‐multidetector row computed tomographic angiography (64‐MDCTA), 3T magnetic resonance (MRI) and standard awake echocardiography. Both anesthetic protocols used propofol for induction and isoflourane for anesthetic maintenance. In addition, protocol A used midazolam/fentanyl and protocol B used dexmedetomedine as premedication and constant rate infusion during the procedure. Significant elevations in systolic and mean blood pressure were present when using protocol B. There was overall good agreement between the variables of cardiac size and systolic function generated from the MDCTA and MRI exams and no significant difference was found when comparing the variables acquired using either anesthetic protocol within each modality. Systolic function variables generated using 64‐MDCTA and 3T MRI were only able to predict the left ventricular end diastolic volume as measured during awake echocardiogram when using protocol B and 64‐MDCTA. For all other systolic function variables, prediction of awake echocardiographic results was not possible (P = 1). Planar variables acquired using MDCTA or MRI did not allow prediction of the corresponding measurements generated using echocardiography in the awake patients (P = 1). Future studies are needed to validate this approach in a more varied population and clinically affected dogs

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area