Computed tomography (CT) scans enable the detection of local enlargements in the abdominal aorta (AA), resulting to straight-forward quantitative and qualitative understandings, typically instated as abdominal aortic aneurysm (AAA). Although, the segmentation of aorta is disposed to stall in presence of expanded lumen or intraluminal thrombus as a result of insufficient spiteful examples, raising the susceptibility for uneventful outcomes of an aortic rupture.
The motion of this research proposes to develop and validate a fully automated deep learning algorithm to segment and measure AAAs on abdominal CT scans. The computer-aided detection (CAD) model is steered by a self-configuring convolutional neural network (CNN), which plumps for essential decisions in a standardised environment to design the 3D segmentation pipeline, regardless of the dataset diversity in the domain. It uses an additional 3D instance-based vertebral segmentation software bundle for independent vertebrae labelling. It coheres with a post-processing routine to perceive the growth patterns by investigation across the aortic centerline around strong anatomical landmarks. It benefits from supplementary measurement techniques of the maximal diameter and cross-section area for gaining extensive insights of the main characteristics of AAA. The system evaluates the relationship between the AA and vertebra level surface features. Conclusively, it generates a portable document, devised to group the anticipated aneurysmal information.
The 3D CAD system agrees with expert’s suggestions about the existence of the aneurysm in 398 institutional images, exhibiting a high capacity to generalize across genders and portions of a full body CT scan using solely radiologist-supported quantitative speculations from the radiology reports. The end-to-end routine achieves an 95.7% dice score coefficient (DSC) on the validation subset for patient-specific cases, indicating a modest agreement with radiologists within an average difference of 0.3 cm in the relative measurement of maximal AAA diameter, thus justifying the possibility of generalizing to the detection of aneurysms using report-based textual information only.