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Purpose: To introduce a computational tool for peri-interventional intracranial aneurysm treatment guidance that maps preoperative planning information from simulation onto real-time X-Ray imaging., Methods: Preoperatively, multiple flow diverter (FD) devices are simulated based on the 3D mesh of the vessel to treat, to choose the optimal size and location. In the peri-operative stage, this 3D information is aligned and mapped to the continuous 2D-X-Ray scan feed from the operating room. The current flow diverter position in the 3D model is estimated by automatically detecting the distal FD marker locations and mapping them to the treated vessel. This allows to visually assess the possible outcome of releasing the device at the current position, and compare it with the one chosen pre-operatively., Results: The full pipeline was validated using retrospectively collected biplane images from four different patients (5 3D-DSA datasets in total). The distal FD marker detector obtained an average F1-score of 0.67 ( ± 0.224 ) in 412 2D-X-Ray scans. After aligning 3D-DSA + 2D-X-Ray datasets, the average difference between simulated and deployed positions was 0.832 mm ( ± 0.521 mm). Finally, we qualitatively show that the proposed approach is able to display the current location of the FD compared to their pre-operatively planned position., Conclusions: The proposed method allows to support the FD deployment procedure by merging and presenting preoperative simulation information to the interventionists, aiding them to make more accurate and less risky decisions., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Purpose: In this paper, we propose to apply generative adversarial neural networks trained with a cycle consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from computed tomography (CT) scans., Methods: A ray-casting US simulation approach is used to generate intermediate synthetic images from abdominal CT scans. Then, an unpaired set of these synthetic and real US images is used to train CycleGANs with two alternative architectures for the generator, a U-Net and a ResNet. These networks are finally used to translate ray-casting based simulations into more realistic synthetic US images., Results: Our approach was evaluated both qualitatively and quantitatively. A user study performed by 21 experts in US imaging shows that both networks significantly improve realism with respect to the original ray-casting algorithm ([Formula: see text]), with the ResNet model performing better than the U-Net ([Formula: see text])., Conclusion: Applying CycleGANs allows to obtain better synthetic US images of the abdomen. These results can contribute to reduce the gap between artificially generated and real US scans, which might positively impact in applications such as semi-supervised training of machine learning algorithms and low-cost training of medical doctors and radiologists in US image interpretation.

Background: Intravascular ultrasound (IVUS) provides axial greyscale images, allowing the assessment of the vessel wall and the surrounding tissues. Several studies have described automatic segmentation of the luminal boundary and the media-adventitia interface by means of different image features., Purpose: The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image features for segmenting IVUS images. The study is focused on Volcano 20 MHz frames not containing plaque or containing fibrotic plaques, and, in principle, it could not be applied to frames containing shadows, calcified plaques, bifurcations and side vessels., Methods: Several image filters, textural descriptors, edge detectors, noise and spatial measures were taken into account. The assessment is based on classification techniques previously used for IVUS segmentation, assigning to each pixel a continuous likelihood value obtained using support vector machines (SVMs). To retrieve relevant features, sequential feature selection was performed guided by the area under the precision-recall curve (AUC-PR)., Results: Subsets of relevant image features for lumen, plaque and surrounding tissues characterization were obtained, and SVMs trained with these features were able to accurately identify those regions. The experimental results were evaluated with respect to ground truth segmentations from a publicly available dataset, reaching values of AUC-PR up to 0.97 and Jaccard index close to 0.85., Conclusion: Noise-reduction filters and Haralick's textural features denoted their relevance to identify lumen and background. Laws' textural features, local binary patterns, Gabor filters and edge detectors had less relevance in the selection process.

In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g. in the diagnosis of diabetic retinopathy, or a nuisance, e.g. in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator. Implementation details are available at http://pages.saclay.inria.fr/ matthew.blaschko/projects/retina/.