Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies

SEGMA: an automatic SEGMentation Approach for human brain MRI using sliding window and random forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course

Multiethnic Exome-Wide Association Study of Subclinical AtherosclerosisCLINICAL PERSPECTIVE

The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease (CHD). We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent CHD

Computer-aided detection and quantification of arterial calcifications with CT

Atherosclerosis is the leading cause of death and disability in the Western world. Arterial calcifications are a marker of the disease and can be detected with computed tomography (CT) scans. In this thesis automatic methods for CT calcium scoring are presented. In CT scans calcifications appear as bright structures, and therefore they were extracted by thresholding and component labeling. However, other high-density objects, such as noise, bony structures, and metal implants were selected by this process as well. To identify true calcifications among all candidate objects, each object was described by features. Those features were derived from the candidate object's location, its appearance, its shape and its size. Subsequently, a pattern recognition approach was used to identify true calcifications among all extracted candidate objects. A first study considered aortic calcifications in CTA scans of the abdomen. All high-density objects in the scan were considered and separated by a pattern recognition system. In terms of the number of calcifications the method resulted in a sensitivity of 83.9% at the expense of on average 1.0 false positive objects per scan. The scan was assigned to one of four categories ("no", "small", "moderate" or "large" amounts of calcification). The correct category label was assigned to 75.0% of scans. A similar approach was used for automatic coronary calcium scoring in non-contrast enhanced, ECG-gated multi-slice CT data. Here, the analysis was performed on the cardiac volume only. In addition to the previously mentioned characteristics, features were also derived from a segmentation of the heart and the aorta, which were extracted automatically using a rule-based scheme. An Agatston score was computed for each scan and subjects were assigned a risk category (0-10, 11-100, 101-400, >400). The correct category was assigned to 93.4% of subjects. Accurate segmentation of the heart and the aorta boundary were found to be essential for the performance of the coronary calcium scoring system. Robust segmentation of these structures is challenging, and therefore a general multi-atlas-based segmentation method was developed. The method was tested on the segmentation of the heart and the aorta in low-dose, non-gated, non-contrast enhanced CT scans of the thorax. The proposed method yielded results very close to those of an independent human observer. Moreover, atlas selection led to faster segmentation at comparable performance. Finally, using the results of the multi-atlas-based segmentation, a system for automatic detection of calcifications in the aorta was developed. In this study the same low-dose, non-gated, non-contrast enhanced CT scans of the thorax were used. Such scans are acquired in lung cancer screening trials and it would be worthwhile to perform calcium scoring in these scans, especially if this could be done automatically. A pattern recognition system was applied to the segmented aortic volume. A correct risk category was assigned to 88.3% of subjects. In conclusion, this thesis presents several systems for computerized detection of arterial calcifications. It is shown that automated calcium scoring is possible and can be used for risk category determination

Automatic whole-heart segmentation in 4D TAVI treatment planning CT

4D cardiac CT angiography (CCTA) images acquired for transcatheter aortic valve implantation (TAVI) planning provide a wealth of information about the morphology of the heart throughout the cardiac cycle. We propose a deep learning method to automatically segment the cardiac chambers and myocardium in 4D CCTA. We obtain automatic segmentations in 472 patients and use these to automatically identify end-systolic (ES) and end-diastolic (ED) phases, and to determine the left ventricular ejection fraction (LVEF). Our results show that automatic segmentation of cardiac structures through the cardiac cycle is feasible (median Dice similarity coefficient 0.908, median average symmetric surface distance 1.59 mm). Moreover, we demonstrate that these segmentations can be used to accurately identify ES and ED phases (bias [limits of agreement] of 1.81 [-11.0; 14.7]% and -0.02 [-14.1; 14.1]%). Finally, we show that there is correspondence between LVEF values determined from CCTA and echocardiography (-1.71 [-25.0; 21.6]%). Our automatic deep learning approach to segmentation has the potential to routinely extract functional information from 4D CCTA

Automatic whole-heart segmentation in 4D TAVI treatment planning CT

4D cardiac CT angiography (CCTA) images acquired for transcatheter aortic valve implantation (TAVI) planning provide a wealth of information about the morphology of the heart throughout the cardiac cycle. We propose a deep learning method to automatically segment the cardiac chambers and myocardium in 4D CCTA. We obtain automatic segmentations in 472 patients and use these to automatically identify end-systolic (ES) and end-diastolic (ED) phases, and to determine the left ventricular ejection fraction (LVEF). Our results show that automatic segmentation of cardiac structures through the cardiac cycle is feasible (median Dice similarity coefficient 0.908, median average symmetric surface distance 1.59 mm). Moreover, we demonstrate that these segmentations can be used to accurately identify ES and ED phases (bias [limits of agreement] of 1.81 [-11.0; 14.7]% and -0.02 [-14.1; 14.1]%). Finally, we show that there is correspondence between LVEF values determined from CCTA and echocardiography (-1.71 [-25.0; 21.6]%). Our automatic deep learning approach to segmentation has the potential to routinely extract functional information from 4D CCTA