A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation

Abstract

Imaging software have become critical tools in the diagnosis and the treatment of abdominal aortic aneurysms (AAA). The aim of this study was to develop a fully automated software system to enable a fast and robust detection of the vascular system and the AAA. The software was designed from a dataset of injected CT-scans images obtained from 40 patients with AAA. Pre-processing steps were performed to reduce the noise of the images using image filters. The border propagation based method was used to localize the aortic lumen. An online error detection was implemented to correct errors due to the propagation in anatomic structures with similar pixel value located close to the aorta. A morphological snake was used to segment 2D or 3D regions. The software allowed an automatic detection of the aortic lumen and the AAA characteristics including the presence of thrombus and calcifications. 2D and 3D reconstructions visualization were available to ease evaluation of both algorithm precision and AAA properties. By enabling a fast and automated detailed analysis of the anatomic characteristics of the AAA, this software could be useful in clinical practice and research and be applied in a large dataset of patients.

3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks

“…However, commercial software are semi-automatic and require user initialization to perform vessel morphological analysis. 6 An automatic tool performing aortic lumen segmentation would facilitate and standardize the analysis of the aortic anatomy, enabling robust and reproducible measurements.…”

Pilvo aortos vietos nustatymas krūtinės ląstos tomografinėje nuotraukoje

“…Atliekant aortos ir trombo segmentaciją aukščiau minėti metodai apjungiami kartu, taip sukuriant aukšto patikimumo vartotojui patogų metodą. Tokio metodo pavyzdys yra [25] sukurtas aortos ir trombo segmentavimo metodas, kuris naudoja ribų sklidimo ir aktyvaus kontūro metodus. Sukurtas metodas buvo įvertintas naudojant 40 kompiuterinės tomografijos vaizdų sekų.…”

Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

“…Applications of image segmentation have been explored in different domains including content-based image retrieval, object detection, face recognition, and medical imaging. More specifically, automated image processing techniques have been used to detect aortic valves using C-arm CTs [23], aortic aneurysms [24], and aortic dissections [25].…”