Evaluation of U-Net Based Architectures for Automatic Aortic Dissection Segmentation

Segmentation and reconstruction of arteries is important for a variety of medical and engineering fields, such as surgical planning and physiological modeling. However, manual methods can be laborious and subject to a high degree of human variability. In this work, we developed various convolutional neural network ( CNN ) architectures to segment Stanford type B aortic dissections ( TBADs ), characterized by a tear in the descending aortic wall creating a normal channel of blood flow called a true lumen and a pathologic channel within the wall called a false lumen. We introduced several variations to the two-dimensional ( 2D ) and three-dimensional (3 D ) U-Net, where small stacks of slices were inputted into the networks instead of individual slices or whole geometries. We compared these variations with a variety of CNN segmentation architectures and found that stacking the input data slices in the upward direction with 2D U-Net improved segmentation accuracy, as measured by the Dice similarity coefficient ( DC ) and point-by-point average distance ( AVD ), by more than 15\% . Our optimal architecture produced DC scores of 0.94, 0.88, and 0.90 and AVD values of 0.074, 0.22, and 0.11 in the whole aorta, true lumen, and false lumen, respectively. Altogether, the predicted reconstructions closely matched manual reconstructions.

Genetic Variation in LRP1 Associates with Stanford Type B Aortic Dissection Risk and Clinical Outcome

Genetic variation in LRP1 (low-density lipoprotein receptor-related protein 1) was reported to be associated with thoracic aortic dissections and aneurysms. The aims of this study were to confirm this association in a prospective single-center patient cohort of patients with acute Stanford type B aortic dissections (STBAD) and to assess the impact of LRP1 variation on clinical outcome. The single nucleotide variation (SNV) rs11172113 within the LRP1 gene was genotyped in 113 STBAD patients and 768 healthy control subjects from the same population. The T-allele of rs11172113 was more common in STBAD patients as compared to the reference group (72.6% vs. 59.6%) and confirmed to be an independent risk factor for STBAD (p = 0.002) after sex and age adjustment in a logistic regression model analyzing diabetes, smoking and hypertension as additional risk factors. Analysis of clinical follow-up (median follow-up 2.0 years) revealed that patients with the T-allele were more likely to suffer aorta-related complications (T-allele 75.6% vs. 63.8%; p = 0.022). In this study sample of STBAD patients, variation in LRP1 was an independent risk factor for STBAD and affected clinical outcome.

Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals

An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients’ chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of (93.9±0.1)% and a sensitivity of (97.5±0.1)%. A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.

Retrograde Carotid Stenting Using Newly Released Venous Stent for Cerebral Malperfusion in Type A Aortic Dissection

Stanford Type A aortic dissections (TAAD) should be considered for repair, given the involvement of branch vessels which can result in malperfusion, specifically cerebral malperfusion secondary to dissection of the innominate and carotid arteries. This is a case report with a focus on four patients presenting with both acute and chronic symptomatic TAAD, with extension into the innominate and common carotid arteries. In all four cases, the decision to intervene utilizing a hybrid endovascular approach was made to increase perfusion to the brain and alleviate symptoms. Through the use of retrograde carotid stenting utilizing both the VICI venous stent (Boston Scientific, Marlborough, MA) and Abre self-expanding Nitinol stent (Medtronic, Minneapolis, MN) we obtained good results, specifically absence of symptoms and return to normal function of the patients.