Increasing the feasibility of minimally invasive procedures in type A aortic dissections: a framework for segmentation and quantification

An efficient procedure for the blood flow computer simulation of patient-specific aortic dissections

In this work we present a novel methodology for the numerical simulation of patient-specific aortic dissections. Our proposal, which targets the seamless virtual prototyping of customized scenarios, combines an innovative two-step segmentation procedure with a CutFEM technique capable of dealing with thin-walled bodies such as the intimal flap. First, we generate the fluid mesh from the outer aortic wall disregarding the intimal flap, similarly to what would be done in a healthy aorta. Second, we create a surface mesh from the approximate midline of the intimal flap. This approach allows us to decouple the segmentation of the fluid volume from that of the intimal flap, thereby bypassing the need to create a volumetric mesh around a thin-walled body, an operation widely known to be complex and error-prone. Once the two meshes are obtained, the original configuration of the dissection into true and false lumen is recovered by embedding the surface mesh into the volumetric one and calculating a level set function that implicitly represents the intimal flap in terms of the volumetric mesh entities. We then leverage the capabilities of unfitted mesh methods, specifically relying on a CutFEM technique tailored for thin-walled bodies, to impose the wall boundary conditions over the embedded intimal flap. We tested the method by simulating the flow in four patient-specific aortic dissections, all involving intricate geometrical patterns. In all cases, the preprocess is greatly simplified with no impact on the computational times. Additionally, the obtained results are consistent with clinical evidence and previous research.

Automatic Segmentation of Type A Aortic Dissection on Computed Tomography Images Using Deep Learning Approach

PURPOSE

Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment.

METHODS

This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared.

RESULTS

Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89-6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35-9.48%).

CONCLUSIONS

The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta.

Implementing a non-local means method to CTA data of aortic dissection

It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.