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• A framework for LRV detection, segmentation in large CMR studies. • Automatic initialization of bi-ventricles for the first time frame without any user intervention. • Automatic End-diastole and End-systole phase assessments. • Supervised deep neural network employed to train and segment the heart. • 3D-ASM image search procedure improved by combining image intensity models with derived distance maps for LV and RV edge localization. The sheer volume of data generated by population imaging studies is unparalleled by current capabilities to extract objective and quantitative cardiac phenotypes; subjective and time-consuming manual image analysis remains the gold standard. Automated image analytics to compute quantitative imaging biomarkers of cardiac function are desperately needed. Data volumes and their variability pose a challenge to most state-of-the-art methods for endo- and epicardial contours, which lack robustness when applied to very large datasets. Our aim is to develop an analysis pipeline for the automatic quantification of cardiac function from cine magnetic resonance imaging data. This work adopt 4,638 cardiac MRI cases coming from UK Biobank with ground truth available for left and RV contours. A hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (three-dimensional active shape models). The contributions of this paper are three-fold. First, a fully automatic method is proposed for left and right ventricle initialization and cardiac MRI segmentation by taking full advantage of spatiotemporal constraint. Second, a deeply supervised network is introduced to train and segment the heart. Third, the 3D-ASM image search procedure is improved by combining image intensity models with convolutional neural network (CNN) derived distance maps improving endo- and epicardial edge localization. The proposed architecture outperformed the state of the art for cardiac MRI segmentation from UK Biobank. The statistics of RV landmarks detection errors for Triscuspid valve and RV apex are 4.17 mm and 5.58 mm separately. The overlap metric, mean contour distance, Hausdorff distance and cardiac functional parameters are calculated for the LV (Left Ventricle) and RV (Right Ventricle) contour segmentation. Bland–Altman analysis for clinical parameters shows that the results from our automated image analysis pipelines are in good agreement with results from expert manual analysis. Our hybrid scheme combines deep learning and statistical shape modeling for automatic segmentation of the LV/RV from cardiac MRI datasets is effective and robust and can compute cardiac functional indexes from population imaging. [ABSTRACT FROM AUTHOR]

• An automatic IQA model for CMR images with different views is presented. • A generalised optimisation-based deep meta-learning model is proposed. • The model can detect respiratory and cardiac motions, aliasing and Gibbs artefacts. • This model is able to address the lack of large annotated datasets. Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are amongst the factors that reveal the necessity of automatic image quality assessment (IQA). However, automated IQA requires access to bulk annotated datasets for training deep learning (DL) models. Labelling medical images is a tedious, costly and time-consuming process, which creates a fundamental challenge in proposing DL-based methods for medical applications. This study aims to present a new method for CMR IQA when there is limited access to annotated datasets. The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks. This model was evaluated on the data of over 6,000 subjects from the UK Biobank for five defined tasks, including detecting respiratory motion, cardiac motion, Aliasing and Gibbs ringing artefacts and images without artefacts. The results of extensive experiments show the superiority of the proposed model. Besides, comparing the model's accuracy with the domain adaptation model indicates a significant difference by using only 64 annotated images related to the desired tasks. The proposed model can identify unknown artefacts in images with acceptable accuracy, which makes it suitable for medical applications and quality assessment of large cohorts. https://github.com/HosseinSimchi/META-IQA-CMRImages [ABSTRACT FROM AUTHOR]

Background and objective: To address the increasing need for collaborative endeavours within the Virtual Physiological Human (VPH) community, the VPH-Share collaborative cloud platform allows researchers to expose and share sequences of complex biomedical processing tasks in the form of computational workflows. The Taverna Workflow System is a very popular tool for orchestrating complex biomedical & bioinformatics processing tasks in the VPH community. This paper describes the VPH-Share components that support the building and execution of Taverna workflows, and explains how they interact with other VPH-Share components to improve the capabilities of the VPH-Share platform. Methods: Taverna workflow support is delivered by the Atmosphere cloud management platform and the VPH-Share Taverna plugin. These components are explained in detail, along with the two main procedures that were developed to enable this seamless integration: workflow composition and execution. Results: 1) Seamless integration of VPH-Share with other components and systems. 2) Extended range of different tools for workflows. 3) Successful integration of scientific workflows from other VPH projects. 4) Execution speed improvement for medical applications. Conclusion: The presented workflow integration provides VPH-Share users with a wide range of different possibilities to compose and execute workflows, such as desktop or online composition, online batch execution, multithreading, remote execution, etc. The specific advantages of each supported tool are presented, as are the roles of Atmosphere and the VPH-Share plugin within the VPH-Share project. The combination of the VPH-Share plugin and Atmosphere engenders the VPH-Share infrastructure with far more flexible, powerful and usable capabilities for the VPH-Share community. As both components can continue to evolve and improve independently, we acknowledge that further improvements are still to be developed and will be described. [ABSTRACT FROM AUTHOR]

• Proposed a 3D patch-based multi-class model for vessel and aneurysm segmentation on 3DRA images. • Proposed cascaded transformer block, multi-view block, learnable downsample block and wide block to help address class imbalance problem. • Designed a multi-class network with weighted Dice loss and set aneurysms as a subclass of vessels to help address inter-class interference. • Designed a new post-processing pipeline including majority voting and self-refinement to make up for the deficiency of patch-based learning then help address inter-institutional data variability. • Robust performance on clinical data from four different sources. The proposed method is superior compared to popular segmentation methods. Background and Objectives: Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico investigation of cerebral hemodynamics within and in the vicinity of aneurysms. However, ensuring precise and robust segmentation of cerebral vessels and aneurysms in neuroimaging modalities such as three-dimensional rotational angiography (3DRA) is challenging. The vasculature constitutes a small proportion of the image volume, resulting in a large class imbalance (relative to surrounding brain tissue). Additionally, aneurysms and vessels have similar image/appearance characteristics, making it challenging to distinguish the aneurysm sac from the vessel lumen. Methods: We propose a novel multi-class convolutional neural network to tackle these challenges and facilitate the automatic segmentation of cerebral vessels and aneurysms in 3DRA images. The proposed model is trained and evaluated on an internal multi-center dataset and an external publicly available challenge dataset. Results: On the internal clinical dataset, our method consistently outperformed several state-of-the-art approaches for vessel and aneurysm segmentation, achieving an average Dice score of 0.81 (0.15 higher than nnUNet) and an average surface-to-surface error of 0.20 mm (less than the in-plane resolution (0.35 mm/pixel)) for aneurysm segmentation; and an average Dice score of 0.91 and average surface-to-surface error of 0.25 mm for vessel segmentation. In 223 cases of a clinical dataset, our method accurately segmented 190 aneurysm cases. Conclusions: The proposed approach can help address class imbalance problems and inter-class interference problems in multi-class segmentation. Besides, this method performs consistently on clinical datasets from four different sources and the generated results are qualified for hemodynamic simulation. Code available at https://github.com/cistib/vessel-aneurysm-segmentation. [ABSTRACT FROM AUTHOR]

Abstract: Determining whether and how an intracranial aneurysm should be treated is a tough decision that clinicians face everyday. Emerging computational tools could help clinicians analyze clinical data and make these decisions. AngioLab is a single graphical user interface, developed on top of the open source framework GIMIAS, that integrates some of the latest image analysis and computational modeling tools for intracranial aneurysms. Two workflows are available: Advanced Morphological Analysis (AMA) and Endovascular Treatment Planning (ETP). AngioLab has been evaluated by a total of 62 clinicians, who considered the information provided by AngioLab relevant and meaningful. They acknowledged the emerging need of these type of tools and the potential impact they might have on the clinical decision-making process. [Copyright &y& Elsevier]