Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms

Abstract

BackgroundAngiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a lesion (eg, an aneurysm sac) by an operator.ObjectiveThe purpose of our work was to determine if a convolutional neural network (CNN) was able to identify and segment the intracranial aneurysm (IA) sac in a DSA and extract API radiomic features with minimal errors compared with human user results.MethodsThree hundred and fifty angiographic images of IAs were retrospectively collected. The IAs and surrounding vasculature were manually contoured and the masks put to a CNN tasked with semantic segmentation. The CNN segmentations were assessed for accuracy using the Dice similarity coefficient (DSC) and Jaccard index (JI). Area under the receiver operating characteristic curve (AUROC) was computed. API features based on the CNN segmentation were compared with the human user results.ResultsThe mean JI was 0.823 (95% CI 0.783 to 0.863) for the IA and 0.737 (95% CI 0.682 to 0.792) for the vasculature. The mean DSC was 0.903 (95% CI 0.867 to 0.937) for the IA and 0.849 (95% CI 0.811 to 0.887) for the vasculature. The mean AUROC was 0.791 (95% CI 0.740 to 0.817) for the IA and 0.715 (95% CI 0.678 to 0.733) for the vasculature. All five API features measured inside the predicted masks were within 18% of those measured inside manually contoured masks.ConclusionsCNN segmentation of IAs and surrounding vasculature from DSA images is non-inferior to manual contours of aneurysms and can be used in parametric imaging procedures.

Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction

“…Third, maintaining the same C-arm angle during acquisition of the pre- and post-treatment sequences would prevent variability in the projection view, allowing for a more consistent relative API correction. These technical adjustments combined with an automatic aneurysm detection and radiomic feature extraction30 could provide a precise technology for decision support in the angiographic suites for the neurosurgeons.…”

Application of radiomics and machine learning in head and neck cancers

“…Second-order features, or texture features, are used to analyze the characteristics of the spatial distribution relationship of voxel intensity between voxels, and can be used to measure heterogeneity within tumors, such as a co-occurrence matrix (GLCM) that could calculates the correlation between two gray levels at a certain distance and a certain direction in an image, calculates the gray-level run length matrix (GLRLM) of continuous voxels with the same intensity in a fixed direction, and the neighborhood gray-level different matrix (NGLDM) between the quantized voxel intensity and the average speed-up intensity of neighboring voxel within a certain distance [45][46][47]. Deep learning is a sub-field of machine learning that has risen to the forefront of artificial intelligence, and one of the most popular deep learning tools available today, the convolutional neural network (CNN), can also be used to extract depth characteristics [48][49]. Convolutional analysis is performed on the image through the CNN, and the data in the fully connected layer is used as the obtained depth feature.…”

Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study

“…[11][12][13][14] Deep learning is an artificial intelligence-based technique using convolutional neural network (CNN) architecture; 15 studies have already demonstrated sensitivities of 93-100% for deep learning algorithms in the detection of aneurysms on MRA. 16,17 Previous deep learning publications focussing on DSA/aneurysms are specifically engineered for aneurysm segmentation, 18 use a two-stage technique to locate specific regions before aneurysm detection, 19 use a spatial information fusion method on 3D rotational angiography 20 or incorporate temporal information from multiple DSA frames. 21 The goal of our study was to determine if commercial-grade deep learning software with previously described applications in mammography 22,23 and radius fracture detection 24 could detect intracranial aneurysms using only standard, wholebrain anteroposterior and lateral projections of DSA.…”