Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: 1) a model that generates synthetic structural errors, and 2) a label appearance simulation network that produces synthetic segmentations (with errors) that are similar in appearance to the real initial segmentations. Using these synthetic segmentations and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net and other previous refinement approaches. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method

Validation of automated Alberta Stroke Program Early CT Score (ASPECTS) software for detection of early ischemic changes on non-contrast brain CT scans

Purpose: In ASPECTS, 10 brain regions are scored visually for presence of acute ischemic stroke damage. We evaluated automated ASPECTS in comparison to expert readers. Methods: Consecutive, baseline non-contrast CT-scans (5-mm slice thickness) from the prospective MR CLEAN trial (n = 459, MR CLEAN Netherlands Trial Registry number: NTR1804) were evaluated. A two-observer consensus for ASPECTS regions (normal/abnormal) was used as reference standard for training and testing (0.2/0.8 division). Two other observers provided individual ASPECTS-region scores. The Automated ASPECTS software was applied. A region score specificity of ≥ 90% was used to determine the software threshold for detection of an affected region based on relative density difference between affected and contralateral region. Sensitivity, specificity, and receiver-operating characteristic curves were calculated. Additionally, we assessed intraclass correlation coefficients (ICCs) for automated ASPECTS and observers in comparison to the reference standard in the test set. Results: In the training set (n = 104), with software thresholds for a specificity of ≥ 90%, we found a sensitivity of 33–49% and an area under the curve (AUC) of 0.741–0.785 for detection of an affected ASPECTS region. In the test set (n = 355), the results for the found software thresholds were 89–89% (specificity), 41–57% (sensitivity), and 0.750–0.795 (AUC). Comparison of automated ASPECTS with the reference standard resulted in an ICC of 0.526. Comparison of observers with the reference standard resulted in an ICC of 0.383–0.464. Conclusion: The performance of automated ASPECTS is comparable to expert readers and could support readers in the detection of early ischemic changes

Time Since Stroke Onset, Quantitative Collateral Score, and Functional Outcome After Endovascular Treatment for Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES: In patients with ischemic stroke undergoing endovascular treatment (EVT), time to treatment and collateral status are important prognostic factors and may be correlated. We aimed to assess the relation between time to CT angiography (CTA) and a quantitatively determined collateral score and to assess whether the collateral score modified the relation between time to recanalization and functional outcome. METHODS: We analyzed data from patients with acute ischemic stroke included in the Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke Registry between 2014 and 2017, who had a carotid terminus or M1 occlusion and were treated with EVT within 6.5 hours of symptom onset. A quantitative collateral score (qCS) was determined from baseline CTA using a validated automated image analysis algorithm. We also determined a 4-point visual collateral score (vCS). Multivariable regression models were used to assess the relations between time to imaging and the qCS and between the time to recanalization and functional outcome (90-day modified Rankin Scale score). An interaction term (time to recanalization × qCS) was entered in the latter model to test whether the qCS modifies this relation. Sensitivity analyses were performed using the vCS. RESULTS: We analyzed 1,813 patients. The median time from symptom onset to CTA was 91 minutes (interquartile range [IQR] 65–150 minutes), and the median qCS was 49% (IQR 25%–78%). Longer time to CTA was not associated with the log-transformed qCS (adjusted β per 30 minutes, 0.002, 95% CI −0.006 to 0.011). Both a higher qCS (adjusted common odds ratio [acOR] per 10% increase: 1.06, 95% CI 1.03–1.09) and shorter time to recanalization (acOR per 30 minutes: 1.17, 95% CI 1.13–1.22) were independently associated with a shift toward better functional outcome. The qCS did not modify the relation between time to recanalization and functional outcome (p for interaction: 0.28). Results from sensitivity analyses using the vCS were similar. DISCUSSION: In the first 6.5 hours of ischemic stroke caused by carotid terminus or M1 occlusion, the collateral status is unaffected by time to imaging, and the benefit of a shorter time to recanalization is independent of baseline collateral status

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

BACKGROUND: Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA). METHODS: Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC). RESULTS: We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. CONCLUSION: The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement

A Randomized Trial of Intravenous Alteplase before Endovascular Treatment for Stroke

The value of administering intravenous alteplase before endovascular treatment (EVT) for acute ischemic stroke has not been studied extensively, particularly in non-Asian populations. METHODS We performed an open-label, multicenter, randomized trial in Europe involving patients with stroke who presented directly to a hospital that was capable of providing EVT and who were eligible for intravenous alteplase and EVT. Patients were randomly assigned in a 1:1 ratio to receive EVT alone or intravenous alteplase followed by EVT (the standard of care). The primary end point was functional outcome on the modified Rankin scale (range, 0 [no disability] to 6 [death]) at 90 days. We assessed the superiority of EVT alone over alteplase plus EVT, as well as noninferiority by a margin of 0.8 for the lower boundary of the 95% confidence interval for the odds ratio of the two trial groups. Death from any cause and symptomatic intracerebral hemorrhage were the main safety end points. RESULTS The analysis included 539 patients. The median score on the modified Rankin scale at 90 days was 3 (interquartile range, 2 to 5) with EVT alone and 2 (interquartile range, 2 to 5) with alteplase plus EVT. The adjusted common odds ratio was 0.84 (95% confidence interval [CI], 0.62 to 1.15; P=0.28), which showed neither superiority nor noninferiority of EVT alone. Mortality was 20.5% with EVT alone and 15.8% with alteplase plus EVT (adjusted odds ratio, 1.39; 95% CI, 0.84 to 2.30). Symptomatic intracerebral hemorrhage occurred in 5.9% and 5.3% of the patients in the respective groups (adjusted odds ratio, 1.30; 95% CI, 0.60 to 2.81). CONCLUSIONS In a randomized trial involving European patients, EVT alone was neither superior nor noninferior to intravenous alteplase followed by EVT with regard to disability outcome at 90 days after stroke. The incidence of symptomatic intracerebral hemorrhage was similar in the two groups

The joint distribution of net worth and pension wealth in Germany

The research on wealth inequality has generally focused on real and financial assets, while giving little attention to pension wealth: the present value of future pension entitlements from public and company pension schemes. This is surprising given the important role pension plans play in guaranteeing material security and well-being for a majority of the population, and suggests that they should be accounted for in peoples’ wealth portfolios. Using novel data from the Socio-Economic Panel (SOEP), we study the incidence, relevance, and distribution of individual pension wealth, net worth, and augmented wealth (the sum of the two) in Germany. Further, we investigate age-wealth profiles and differences between East and West Germany

Siamese model for collateral score prediction from computed tomography angiography images in acute ischemic stroke

INTRODUCTION: Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models.METHODS: The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score.EXPERIMENTS: Several experiments have been performed to optimize the model hyperparameters and training procedure, and to validate the final model performance. The hyperparameter optimization and subsequent model training was done using CTA images from the MR CLEAN Registry, a Dutch multi-center multi-vendor registry of acute stroke patients that underwent endovascular treatment. A separate set of images, from the MR CLEAN Trial, served as an external validation set, where collateral scoring was assessed and compared with both human observers and a recent more traditional model. In addition, the automated collateral scores have been used in an existing functional outcome prediction model that uses both imaging and non-imaging clinical parameters.CONCLUSION: The results show that end-to-end learning of collateral scoring in CTA images is feasible, and does perform similar to more traditional methods, and the performance also is within the inter-observer variation. Furthermore, the results demonstrate that the end-to-end classification results also can be used in an existing functional outcome prediction model.</p

Siamese model for collateral score prediction from computed tomography angiography images in acute ischemic stroke

INTRODUCTION: Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models.METHODS: The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score.EXPERIMENTS: Several experiments have been performed to optimize the model hyperparameters and training procedure, and to validate the final model performance. The hyperparameter optimization and subsequent model training was done using CTA images from the MR CLEAN Registry, a Dutch multi-center multi-vendor registry of acute stroke patients that underwent endovascular treatment. A separate set of images, from the MR CLEAN Trial, served as an external validation set, where collateral scoring was assessed and compared with both human observers and a recent more traditional model. In addition, the automated collateral scores have been used in an existing functional outcome prediction model that uses both imaging and non-imaging clinical parameters.CONCLUSION: The results show that end-to-end learning of collateral scoring in CTA images is feasible, and does perform similar to more traditional methods, and the performance also is within the inter-observer variation. Furthermore, the results demonstrate that the end-to-end classification results also can be used in an existing functional outcome prediction model.</p