Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations

Computed Tomography Angiography is a key modality providing insights into the cerebrovascular vessel tree that are crucial for the diagnosis and treatment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data. Using only masks as the input to our model uniquely allows us to apply such deformations much more aggressively than one could with conventional image volumes while retaining sample realism. The neural network classifies the presence of an LVO and the affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets. Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85 from a baseline value of 0.56 using no augmentation. The best performance was achieved using a 3D-DenseNet yielding an AUC of 0.87. The augmentation had positive impact in classification of the affected hemisphere as well, where the 3D-DenseNet reached an AUC of 0.93 on both sides.Comment: 7 pages. Accepted at BVM-Workshop 2022, Springe

Czech Pattern Recognition Society Partially Rigid Bone Registration in CT Angiography

Abstract Maximum intensity projection (MIP) studies of CT angiography (CTA) images are a widely used tool for artery and vein visualization especially in the brain. Due to their high CT intensity bone structures lead to visualization artifacts in MIP studies, therefore they have to be removed to get an undistorted view of the vessel structures. Often this removal is possible by a rigid registration step of an additional native scan to the contrast-enhanced CTA scan followed by a bone mask subtraction. This technique is also referred to as ”Matched Mask Bone Elimination ” [14]. However, sometimes several unrelated patient movements occur during and between contrast-enhanced and native scans. These intra- and inter-scan motion artifacts cannot be removed by a single rigid registration step. To address these problems in an efficient and general way we have developed a refinement of the ”Matched Mask Bone Elimination ” technique that incorporates a joint segmentation and registration method in an iterative fashion. We describe our experiments and show our qualitative and quantitative results in terms of decreasing numbers of misregistration voxels and sum of squared intensity differences on several large volume data sets of the head, where independent rigid movements have been successfully removed.

SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic

Abstract. Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm.

Accuracy of Non-Enhanced CT in Detecting Early Ischemic Edema Using Frequency Selective Non-Linear Blending.

PURPOSE:Ischemic brain edema is subtle and hard to detect by computed tomography within the first hours of stroke onset. We hypothesize that non-enhanced CT (NECT) post-processing with frequency-selective non-linear blending ("best contrast"/BC) increases its accuracy in detecting edema and irreversible tissue damage (infarction). METHODS:We retrospectively analyzed the NECT scans of 76 consecutive patients with ischemic stroke (exclusively middle cerebral artery territory-MCA) before and after post-processing with BC both at baseline before reperfusion therapy and at follow-up (5.73±12.74 days after stroke onset) using the Alberta Stroke Program Early CT Score (ASPECTS). We assessed the differences in ASPECTS between unprocessed and post-processed images and calculated sensitivity, specificity, and predictive values of baseline NECT using follow-up CT serving as reference standard for brain infarction. RESULTS:NECT detected brain tissue hypoattenuation in 35 of 76 patients (46.1%). This number increased to 71 patients (93.4%) after post-processing with BC. Follow-up NECT confirmed brain infarctions in 65 patients (85.5%; p = 0.012). Post-processing increased the sensitivity of NECT for brain infarction from 35/65 (54%) to 65/65 (100%), decreased its specificity from 11/11 (100%) to 7/11 (64%), its positive predictive value (PPV) from 35/35 (100%) to 65/69 (94%) and increased its accuracy 46/76 (61%) to 72/76 (95%). CONCLUSIONS:This post-hoc analysis suggests that post-processing of NECT with BC may increase its sensitivity for ischemic brain damage significantly

Segmental bronchi collapsibility: computed tomography-based quantification in patients with chronic obstructive pulmonary disease and correlation with emphysema phenotype, corresponding lung volume changes and clinical parameters

BACKGROUND: Global pulmonary function tests lack region specific differentiation that might influence therapy in severe chronic obstructive pulmonary disease (COPD) patients. Therefore, the aim of this work was to assess the degree of expiratory 3(rd) generation bronchial lumen collapsibility in patients with severe COPD using chest-computed tomography (CT), to evaluate emphysema-phenotype, lobar volumes and correlate results with pulmonary function tests. METHODS: Thin-slice chest-CTs acquired at end-inspiration & end-expiration in 42 COPD GOLD IV patients (19 females, median-age: 65.9 y) from November 2011 to July 2014 were re-evaluated. The cross-sectional area of all segmental bronchi was measured 5 mm below the bronchial origin in both examinations. Lung lobes were semi-automatically segmented, volumes calculated at end-inspiratory and end-expiratory phase and visually defined emphysema-phenotypes defined. Results of CT densitometry were compared with lung functional tests including forced expiratory volume at 1 s (FEV(1)), total lung capacity (TLC), vital capacity (VC), residual volume (RV), diffusion capacity parameters and the maximal expiratory flow rates (MEFs). RESULTS: Mean expiratory bronchial collapse was 31%, stronger in lobes with homogenous (38.5%) vs. heterogeneous emphysema-phenotype (27.8%, P=0.014). The mean lobar expiratory volume reduction was comparable in both emphysema-phenotypes (volume reduction 18.6%±8.3% in homogenous vs. 17.6%±16.5% in heterogeneous phenotype). The degree of bronchial lumen collapsibility, did not correlate with expiratory volume reduction. MEF(25) correlated weakly with 3(rd) generation airway collapsibility (r=0.339, P=0.03). All patients showed a concentric expiratory reduction of bronchial cross-sectional area. CONCLUSIONS: Changes in collapsibility of 3(rd) generation bronchi in COPD grade IV patients is significantly lower than that in the trachea and the main bronchi. Collapsibility did not correlate with the reduction in lung volume but was significantly higher in lobes with homogeneous vs. heterogeneous emphysema phenotype. Changes in the 3(rd) generation bronchial calibres between inspiration and expiration are not predictive for the degree of small airway collapsibility and related airflow limitation

Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection

Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strokes. A large training data set is required for a standard deep learning-based model to learn this strategy from data. As labeled medical data in this field is rare, other approaches need to be developed. To both include the prior knowledge of side comparison and increase the amount of training data, we propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres or hemisphere subregions from different patients. The subregions cover vessels commonly affected by LVOs, namely the internal carotid artery (ICA) and middle cerebral artery (MCA). In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres. Furthermore, we propose an extension of that architecture to process the individual hemisphere subregions. All configurations predict the presence of an LVO, its side, and the affected subregion. We show the effect of recombination as an augmentation strategy in a 5-fold cross validated ablation study. We enhanced the AUC for patient-wise classification regarding the presence of an LVO of all investigated architectures. For one variant, the proposed method improved the AUC from 0.73 without augmentation to 0.89. The best configuration detects LVOs with an AUC of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while accurately predicting the affected side.Comment: PrePrint - Accepted at MICCAI 202

Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT

(1) Background: to test the diagnostic performance of a fully convolutional neural network-based software prototype for clot detection in intracranial arteries using non-enhanced computed tomography (NECT) imaging data. (2) Methods: we retrospectively identified 85 patients with stroke imaging and one intracranial vessel occlusion. An automated clot detection prototype computed clot location, clot length, and clot volume in NECT scans. Clot detection rates were compared to the visual assessment of the hyperdense artery sign by two neuroradiologists. CT angiography (CTA) was used as the ground truth. Additionally, NIHSS, ASPECTS, type of therapy, and TOAST were registered to assess the relationship between clinical parameters, image results, and chosen therapy. (3) Results: the overall detection rate of the software was 66%, while the human readers had lower rates of 46% and 24%, respectively. Clot detection rates of the automated software were best in the proximal middle cerebral artery (MCA) and the intracranial carotid artery (ICA) with 88–92% followed by the more distal MCA and basilar artery with 67–69%. There was a high correlation between greater clot length and interventional thrombectomy and between smaller clot length and rather conservative treatment. (4) Conclusions: the automated clot detection prototype has the potential to detect intracranial arterial thromboembolism in NECT images, particularly in the ICA and MCA. Thus, it could support radiologists in emergency settings to speed up the diagnosis of acute ischemic stroke, especially in settings where CTA is not available