Automated image fusion during endovascular aneurysm repair: a feasibility and accuracy study

Purpose: Image fusion merges preoperative computed tomography angiography (CTA) with live fluoroscopy during endovascular procedures to function as an overlay 3D roadmap. However, in most current systems, the registration between imaging modalities is performed manually by vertebral column matching which can be subjective, inaccurate and time consuming depending on experience. Our objective was to evaluate feasibility and accuracy of image-based automated 2D-3D image fusion between preoperative CTA and intraoperative fluoroscopy based on vertebral column matching. Methods: A single-center study with offline procedure data was conducted in 10 consecutive patients which had endovascular aortic repair in which we evaluated unreleased automated fusion software provided by Philips (Best, the Netherlands). Fluoroscopy and digital subtraction angiography images were collected after the procedures and the vertebral column was fused fully automatically. Primary endpoints were feasibility and accuracy of bone alignment (mm). Secondary endpoint was vascular alignment (mm) between the lowest renal artery orifices. Clinical non-inferiority was defined at a mismatch of < 1 mm. Results: In total, 87 automated measurements and 40 manual measurements were performed on vertebrae T12–L5 in all 10 patients. Manual correction was needed in 3 of the 10 patients due to incomplete visibility of the vertebral edges in the fluoroscopy image. Median difference between automated fusion and manual fusion was 0.1 mm for bone alignment (p = 0.94). The vascular alignment was 4.9 mm (0.7–17.5 mm) for manual and 5.5 mm (1.0–14.0 mm) for automated fusion. This did not improve, due to the presence of stiff wires and stent graft. Conclusion: Automated image fusion was feasible when all vertebral edges were visible. Accuracy was non-inferior to manual image fusion regarding bone alignment. Future developments should focus on intraoperative image-based correction of vascular alignment

Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images

We propose a novel Brain-Inspired Multi-Scales and Multi-Orientations (BIMSO) segmentation technique for the retinal images taken with laser ophthalmoscope (SLO) imaging cameras. Conventional retinal segmentation methods have been designed mainly for color RGB images and they often fail in segmenting the SLO images because of the presence of noise in these images. We suppress the noise and enhance the blood vessels by lifting the 2D image to a joint space of positions and orientations (SE(2)) using the directional anisotropic wavelets. Then a neural network classifier is trained and tested using several features including the intensity of pixels, filter response to the wavelet and multi-scale left-invariant Gaussian derivatives jet in SE(2). BIMSO is robust against noise, non-uniform luminosity and contrast variability. In addition to preserving the connections, it has higher sensitivity and detects the small vessels better compared to state-of-the-art methods for both RGB and SLO images

Accuracy assessment of CBCT-based volumetric brain shift field

\u3cp\u3eThe displacement of the brain parenchyma during open brain surgery, known as ‘brain shift’, affects the applicability of pre-operative planning and affects the outcome of the surgery. In this article we investigated the accuracy of a novel method to intra-operatively determine the brain shift displacement field throughout the whole brain volume. The brain shift displacement was determined by acquiring contrast enhanced cone-beam CT before and during the surgery. The respective datasets were pre-processed, landmark enhanced, and elastically registered to find the displacement field. The accuracy of this method was evaluated by artificially creating post-operative data with a known ground truth deformation. The artificial post-operative data was obtained by applying the deformation field from one patient on the pre-operative data of another patient, which was repeated for three patients. The mean error that was found with this method ranged from 1 to 2 mm, while the standard deviation was about 1 mm.\u3c/p\u3

Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores

Several ocular and systemic diseases such as hypertension and arteriosclerosis cause geometrical and functional changes to the vasculature in retinal images, including alterations in the shape of vascular bifurcations and crossings. To use the diagnostic information of the junctions, it is important to detect them first. In this work, a novel BIfurcation and CRossing detection method using Orientations Scores (BICROS) is introduced. The Brain-inspired orientation score transformation lifts the image to the joint space of positions and orientations using directional anisotropic wavelets. Candidate junctions are selected based on their geometrical properties in this space. Then false detections are eliminated in a supervised manner. Additionally, a more conventional pipeline for junction detection based on morphological analysis of vessel segmentations is included. Finally, both approaches are combined and the resulting junctions are classified into bifurcations and crossings. The proposed method outperforms state of the art on a public and private dataset

Brain-inspired algorithms for retinal image analysis

Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power