Mesh convolutional neural networks for wall shear stress estimation in 3D artery models

Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit large-scale use in practice. Recently, the use of deep learning for rapid estimation of CFD parameters like wall shear stress (WSS) on surface meshes has been investigated. However, existing approaches typically depend on a hand-crafted re-parametrisation of the surface mesh to match convolutional neural network architectures. In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD. We train and evaluate our method on two datasets of synthetic coronary artery models with and without bifurcation, using a ground truth obtained from CFD simulation. We show that our flexible deep learning model can accurately predict 3D WSS vectors on this surface mesh. Our method processes new meshes in less than 5 [s], consistently achieves a normalised mean absolute error of $\leq$ 1.6 [%], and peaks at 90.5 [%] median approximation accuracy over the held-out test set, comparing favourably to previously published work. This demonstrates the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models.Comment: (MICCAI 2021) Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM). The final authenticated version is available on SpringerLin

Going Off-Grid: Implicit Neural Representations for 3D Vascular Modeling

Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been constructed with explicit representations such as meshes and voxel masks, or implicit representations such as radial basis functions or atomic (tubular) shapes. Here, we propose to represent surfaces by the zero level set of their signed distance function (SDF) in a differentiable implicit neural representation (INR). This allows us to model complex vascular structures with a representation that is implicit, continuous, light-weight, and easy to integrate with deep learning algorithms. We here demonstrate the potential of this approach with three practical examples. First, we obtain an accurate and watertight surface for an abdominal aortic aneurysm (AAA) from CT images and show robust fitting from as little as 200 points on the surface. Second, we simultaneously fit nested vessel walls in a single INR without intersections. Third, we show how 3D models of individual arteries can be smoothly blended into a single watertight surface. Our results show that INRs are a flexible representation with potential for minimally interactive annotation and manipulation of complex vascular structures.Comment: MICCAI STACOM 202

Anatomy-aided deep learning for medical image segmentation: A review

Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatomical information which is the crucial cue for manual segmentation. In this paper, we provide a review of anatomy-aided DL for medical image segmentation which covers systematically summarized anatomical information categories and corresponding representation methods. We address known and potentially solvable challenges in anatomy-aided DL and present a categorized methodology overview on using anatomical information with DL from over 70 papers. Finally, we discuss the strengths and limitations of the current anatomy-aided DL approaches and suggest potential future work

Mesh convolutional neural networks for wall shear stress estimation in 3D artery models

Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit large-scale use in practice. Recently, the use of deep learning for rapid estimation of CFD parameters like wall shear stress (WSS) on surface meshes has been investigated. However, existing approaches typically depend on a hand-crafted re-parametrisation of the surface mesh to match convolutional neural network architectures. In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD. We train and evaluate our method on two datasets of synthetic coronary artery models with and without bifurcation, using a ground truth obtained from CFD simulation. We show that our flexible deep learning model can accurately predict 3D WSS vectors on this surface mesh. Our method processes new meshes in less than 5 [s], consistently achieves a normalised mean absolute error of ≤1.6 [%], and peaks at 90.5 [%] median approximation accuracy over the held-out test set, comparing favourably to previously published work. This demonstrates the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models

Commonly available hematological biomarkers are associated with the extent of coronary calcifications

Background and aims: We aimed to improve the understanding of potential associations between commonly available hematological biomarkers and the coronary artery calcification (CAC) score, which may help unravel the pathophysiology of coronary calcifications and subclinical coronary artery disease. Methods: A cross-sectional study was performed within the Utrecht Patient Oriented Database (UPOD). Patients with suspected or known coronary artery disease who underwent CT CAC scoring as well as standard hematology analysis that was part of routine clinical care (within 3 months of CT acquisition) were included. Complete hematology datasets were extracted from hematology analyzers. Linear regression adjusted for potential confounders was used to assess if hematological biomarkers were related to the CAC score. Results: In total, 1504 patients were included, of whom 43% (n = 647) had a CAC score of 0. Mean age (±SD) was 53 ± 13 years, and 34% of patients were women. Red blood cell distribution width (RDW, β = 0.20 [0.05–0.36], p=0.007), the fraction of immature reticulocytes (β = 0.97 [0.10–6.43], p=0.004), coefficient of variation of neutrophil lobularity (β = 0.13 [0.01–0.25], p=0.040) and mean lymphocyte cell size (β = 0.21 [0.08–0.34], p=0.001) were positively associated with the CAC score after adjustment for age, sex, body mass index (BMI), diabetes, glomerular filtration rate (GFR) and high-density lipoprotein (HDL). Conclusions: This study confirms the known association of RDW with the CAC score, and presents the fraction of immature reticulocytes, coefficient of variation of neutrophil lobularity, and mean lymphocyte cell size as new markers associated with a higher CAC score

Commonly available hematological biomarkers are associated with the extent of coronary calcifications

Background and aims: We aimed to improve the understanding of potential associations between commonly available hematological biomarkers and the coronary artery calcification (CAC) score, which may help unravel the pathophysiology of coronary calcifications and subclinical coronary artery disease. Methods: A cross-sectional study was performed within the Utrecht Patient Oriented Database (UPOD). Patients with suspected or known coronary artery disease who underwent CT CAC scoring as well as standard hematology analysis that was part of routine clinical care (within 3 months of CT acquisition) were included. Complete hematology datasets were extracted from hematology analyzers. Linear regression adjusted for potential confounders was used to assess if hematological biomarkers were related to the CAC score. Results: In total, 1504 patients were included, of whom 43% (n = 647) had a CAC score of 0. Mean age (±SD) was 53 ± 13 years, and 34% of patients were women. Red blood cell distribution width (RDW, β = 0.20 [0.05–0.36], p=0.007), the fraction of immature reticulocytes (β = 0.97 [0.10–6.43], p=0.004), coefficient of variation of neutrophil lobularity (β = 0.13 [0.01–0.25], p=0.040) and mean lymphocyte cell size (β = 0.21 [0.08–0.34], p=0.001) were positively associated with the CAC score after adjustment for age, sex, body mass index (BMI), diabetes, glomerular filtration rate (GFR) and high-density lipoprotein (HDL). Conclusions: This study confirms the known association of RDW with the CAC score, and presents the fraction of immature reticulocytes, coefficient of variation of neutrophil lobularity, and mean lymphocyte cell size as new markers associated with a higher CAC score

Commonly available hematological biomarkers are associated with the extent of coronary calcifications

Background and aims: We aimed to improve the understanding of potential associations between commonly available hematological biomarkers and the coronary artery calcification (CAC) score, which may help unravel the pathophysiology of coronary calcifications and subclinical coronary artery disease. Methods: A cross-sectional study was performed within the Utrecht Patient Oriented Database (UPOD). Patients with suspected or known coronary artery disease who underwent CT CAC scoring as well as standard hematology analysis that was part of routine clinical care (within 3 months of CT acquisition) were included. Complete hematology datasets were extracted from hematology analyzers. Linear regression adjusted for potential confounders was used to assess if hematological biomarkers were related to the CAC score. Results: In total, 1504 patients were included, of whom 43% (n = 647) had a CAC score of 0. Mean age (±SD) was 53 ± 13 years, and 34% of patients were women. Red blood cell distribution width (RDW, β = 0.20 [0.05–0.36], p=0.007), the fraction of immature reticulocytes (β = 0.97 [0.10–6.43], p=0.004), coefficient of variation of neutrophil lobularity (β = 0.13 [0.01–0.25], p=0.040) and mean lymphocyte cell size (β = 0.21 [0.08–0.34], p=0.001) were positively associated with the CAC score after adjustment for age, sex, body mass index (BMI), diabetes, glomerular filtration rate (GFR) and high-density lipoprotein (HDL). Conclusions: This study confirms the known association of RDW with the CAC score, and presents the fraction of immature reticulocytes, coefficient of variation of neutrophil lobularity, and mean lymphocyte cell size as new markers associated with a higher CAC score