Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, an event that is often fatal. Human evaluation of IA rupture is rather subjective, therefore we aimed to develop a deep learning based rupture event prediction model from aneurysm shape information. We used 386 CTA scans with 500 IAs that were either unruptured or ruptured (250/250). The IA rupture status was computed using bottleneck layer feature vectors sourced from two deep learning models trained for the respective auxiliary tasks of cerebral vessel labeling and aneurysm isolation from its parent vessels. The two extracted feature vectors were concatenated with 20 established features (patient sex, age, aneurysm location and morphological parameters) and used to predict aneurysm rupture status using eight different machine learning models. We achieved the best AUC of 0.851 using random forest with feature selection based on Spearman’s rank correlation thresholding. The rather good performance of IA rupture status classification renders the proposed approach as a promising tool for management of rupture risk in the ”no treatment” paradigm of patient follow-up imaging.
Introduction: Vascular diseases, such as intracranial aneurysms, are one of the top causes of death in the world. Due to the constantly increasing number of angiographic imaging examinations and their use in population screening there is a need for accurate and robust methods for vessel segmentation. Methods & Materials: We used a publicly available dataset of 570 cerebral TOF-MRA angiograms (IXI dataset) and manually created reference segmentations using interactive thresholding of the raw and vesselness filter enhanced angiograms. The obtained segmentations were visually verified by a skilled radiologist and then used to objectively and comparatively evaluate six approaches based on recent convolutional neural network (CNN) segmentation models. Results: Model training on raw images (without preprocessing) resulted in Dice similarity coefficient (DSC) value of 0.91, while preprocessing with specialized filters produced inferior DSC values. Spatially affixed model training on the Circle of Willis (CoW) region yielded a significantly better result (DSC=0.95; p-value < 0.001) as compared to the training on whole images (DSC=0.91). Conclusion: On the MRA scans of IXI dataset we created reference vessel segmentations to serve as a new benchmark for vessel segmentation studies. The reference segmentations are publicly available**. Among six state-of-the-art approaches evaluated on this dataset, we found that raw input images with spatially affixed CNN model training with respect to CoW achieved the best vessel segmentation.
Intracranial aneurysms (IAs) are mostly asymptomatic and thus often discovered incidentally on angiographic scans like 3D DSA, CTA and MRA. Skilled radiologists achieved a sensitivity of 88% by means of visual detection, which seems inadequate considering that prevalence of IAs in general population is 3-5%. Deep learning models trained and executed on angiographic scans seem best-suited for IA detection, however, reported performances across different modalities is currently insufficient for clinical application. This paper presents a novel modality agnostic method for detection of IAs. First the triangulated surfaces of vascular structures were roughly extracted from the angiograms. For IA detection purpose, the extracted surfaces were randomly parcellated into local patches and then a translation, rotation and scale invariant classifier based on deep neural network (DNN) was trained. Test stage proceeded by mimicking the surface extraction and parcellation at several random locations, then the trained DNN model was applied for classification, and the results aggregated into IA detection heatmaps across entire vascular surface. For training and validation the extracted contours were presented to skilled neurosurgeon, who marked the locations of IAs. The DNN was trained and tested using three-fold cross-validation based on 57 DSAs, 5 CTAs and 5 MRAs and showed a 98.6% sensitivity at 0.2 false positive detections per image. Experimental results show that proposed approach not only significantly improved detection sensitivity and specificity compared to state-of-the-art intensity based methods, but is also modality agnostic and thus better suited for clinical application.
As growing aneurysms are very likely to rupture, features to detect and quantify the growth are needed in order to assess rupture risk. So far cross-sectional features like maximum dome size were used, however, independent analysis of baseline and follow-up aneurysm shapes may bias these features and thereby conceal the often subtle changes of aneurysm morphology. We propose to detect and quantify aneurysm growth using shape coregistration, composed of globally optimal rigid registration, followed by non-rigid warping of baseline mesh to the follow-up mesh. Aneurysm isolation algorithm is used to constrain the registration to parent vessels and to aneurysm dome in the rigid and non-rigid registration steps, respectively. Based on the analysis of the obtained deformation field, two novel morphologic features were proposed, namely the relative differential surface area and median path length, normalized by maximum dome size. The morphological features were extracted and studied on a CTA image dataset of 20 patients, each containing one unruptured intracranial saccular aneurysm (maximal dome diameters were from 1.4 to 12.2 mm). For a baseline performance comparison, five cross-sectional features were also extracted and their relative change computed. The two novel registration based features performed best as demonstrated by lowest p-values (<0.003) obtained by Mann-Whitney U-test and highest area under the curve (>0.89) obtained from a ROC analysis. The proposed differential features are inherently longitudinal, taking into consideration baseline and follow-up aneurysm shape information at once, and seem to enable an interventional neuroradiologist to differentiate better between low- and high-rupture-risk aneurysms.
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