Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity

The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators

Nano-building block based-hybrid organic–inorganic copolymers with self-healing properties

New dynamic materials, that can repair themselves after strong damage, have been designed by hybridization of polymers with structurally well-defined nanobuilding units. The controlled design of cross-linked poly(n-butyl acrylate) (pBuA) has been performed by introducing a very low amount of a specific tin oxo-cluster. Sacrificial domains with non-covalent interactions (i.e. ionic bonds) developed at the hybrid interface play a double role. Such interactions are strong enough to cross-link the polymer, which consequently exhibits rubber-like elasticity behavior and labile enough to enable, after severe mechanical damage, dynamic bond recombination leading to an efficient healing process at room temperature. In agreement with the nature of the reversible links at the hybrid interface, the healing process can speed up considerably with temperature. 1H and 119Sn PFG NMR has been used to evidence the dynamic nature of these peculiar cross-linking nodes

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity. In this paper, we propose a fully automated deep learning method that performs image-level tortuosity grading of corneal nerves, which is based on CCM images and segmented corneal nerves to further improve the grading accuracy with interpretability principles. The proposed method consists of two stages: 1) A pre-trained feature extraction backbone over ImageNet is fine-tuned with a proposed novel bilinear attention (BA) module for the prediction of the regions of interest (ROIs) and coarse grading of the image. The BA module enhances the ability of the network to model long-range dependencies and global contexts of nerve fibers by capturing second-order statistics of high-level features. 2) An auxiliary tortuosity grading network (AuxNet) is proposed to obtain an auxiliary grading over the identified ROIs, enabling the coarse and additional gradings to be finally fused together for more accurate final results. The experimental results show that our method surpasses existing methods in tortuosity grading, and achieves an overall accuracy of 85.64% in four-level classification. We also validate it over a clinical dataset, and the statistical analysis demonstrates a significant difference of tortuosity levels between healthy control and diabetes group. We have released a dataset with 1500 CCM images and their manual annotations of four tortuosity levels for public access. The code is available at: https://github.com/iMED-Lab/TortuosityGrading

Distributed quantitative evaluation of 3D patient specific arterial models

In this paper we describe a new system for the 3D reconstruction and distribution on the net of models for vessels structures. The system is specifically designed to support measurements of medical interest. We describe 2D and 3D segmentation methods implemented and the procedure used to build interactive VRML97 models. The experimental section presents a comparison between segmentation methods, and a first application to surgical planning for endovascular repair of Abdominal Aortic Aneurysms