Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field

Radiomics-Based Classification of Left Ventricular Non-compaction, Hypertrophic Cardiomyopathy, and Dilated Cardiomyopathy in Cardiovascular Magnetic Resonance

Miocardiopatía dilatada; Miocardiopatía hipertrófica; RadiómicaDilated cardiomyopathy; Hypertrophic cardiomyopathy; RadiomicsMiocardiopatia dilatada; Miocardiopatia hipertròfica; RadiòmicaLeft Ventricular (LV) Non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) share morphological and functional traits that increase the diagnosis complexity. Additional clinical information, besides imaging data such as cardiovascular magnetic resonance (CMR), is usually required to reach a definitive diagnosis, including electrocardiography (ECG), family history, and genetics. Alternatively, indices of hypertrabeculation have been introduced, but they require tedious and time-consuming delineations of the trabeculae on the CMR images. In this paper, we propose a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions. A total of 118 subjects, including 35 patients with LVNC, 25 with HCM, 37 with DCM, as well as 21 healthy volunteers (NOR), underwent CMR imaging. A comprehensive radiomics characterization was applied to LV short-axis images to quantify shape, first-order, co-occurrence matrix, run-length matrix, and local binary patterns. Conventional CMR indices (LV volumes, mass, wall thickness, LV ejection fraction—LVEF—), as well as hypertrabeculation indices by Petersen and Jacquier, were also analyzed. State-of-the-art Machine Learning (ML) models (one-vs.-rest Support Vector Machine—SVM—, Logistic Regression—LR—, and Random Forest Classifier—RF—) were used for one-vs.-rest classification tasks. The use of radiomics models for the automated diagnosis of LVNC, HCM, and DCM resulted in excellent one-vs.-rest ROC-AUC values of 0.95 while generating these results without the need for the delineation of the trabeculae. First-order and texture features resulted to be among the most discriminative features in the obtained radiomics signatures, indicating their added value for quantifying relevant tissue patterns in cardiomyopathy differential diagnosis.This publication was partially funded by the European Union's Horizon 2020 research and innovation euCanSHare project under grant agreement No 825903. KL received funding from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-099898-B-I00. AG has received funding from the Spanish Ministry of Science, Innovation and Universities (IJC2018-037349-I)

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field

Cardiometabolic risk estimation using exposome data and machine learning

Background: The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. Objective: Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. Methods: From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. Results: The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. Conclusions: We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.</p

New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics

Background: Ischaemic heart disease (IHD) and cerebrovascular disease are two closely inter-related clinical entities. Cardiovascular magnetic resonance (CMR) radiomics may capture subtle cardiac changes associated with these two diseases providing new insights into the brain-heart interactions.Objective: To define the CMR radiomics signatures for IHD and cerebrovascular disease and study their incremental value for disease discrimination over conventional CMR indices.Methods: We analysed CMR images of UK Biobank's subjects with pre-existing IHD, ischaemic cerebrovascular disease, myocardial infarction (MI), and ischaemic stroke (IS) (n = 779, 267, 525, and 107, respectively). Each disease group was compared with an equal number of healthy controls. We extracted 446 shape, first-order, and texture radiomics features from three regions of interest (right ventricle, left ventricle, and left ventricular myocardium) in end-diastole and end-systole defined from segmentation of short-axis cine images. Systematic feature selection combined with machine learning (ML) algorithms (support vector machine and random forest) and 10-fold cross-validation tests were used to build the radiomics signature for each condition. We compared the discriminatory power achieved by the radiomics signature with conventional indices for each disease group, using the area under the curve (AUC), receiver operating characteristic (ROC) analysis, and paired t-test for statistical significance. A third model combining both radiomics and conventional indices was also evaluated.Results: In all the study groups, radiomics signatures provided a significantly better disease discrimination than conventional indices, as suggested by AUC (IHD:0.82 vs. 0.75; cerebrovascular disease: 0.79 vs. 0.77; MI: 0.87 vs. 0.79, and IS: 0.81 vs. 0.72). Similar results were observed with the combined models. In IHD and MI, LV shape radiomics were dominant. However, in IS and cerebrovascular disease, the combination of shape and intensity-based features improved the disease discrimination. A notable overlap of the radiomics signatures of IHD and cerebrovascular disease was also found.Conclusions: This study demonstrates the potential value of CMR radiomics over conventional indices in detecting subtle cardiac changes associated with chronic ischaemic processes involving the brain and heart, even in the presence of more heterogeneous clinical pictures. Radiomics analysis might also improve our understanding of the complex mechanisms behind the brain-heart interactions during ischaemia

Estimation of biological heart age using cardiovascular magnetic resonance radiomics

We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-diferential patterns. We subtracted actual age from model estimated heart age to calculate a “heart age delta”, which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field

Image Analysis and Modelling of the Infarcted Heart Response at the Microvascular Level

The coronary microvasculature comprises the smallest blood vessels of the cardiac tissue. It continuously adapts in response to physiological and pathophysiological conditions to meet tissue demands. Quantitative assessment of the dynamic changes taking place in the coronary microvasculature is therefore crucial in enhancing our knowledge regarding the impact of cardiovascular disease on tissue perfusion and on developing efficient angiotherapies. This thesis focuses on deciphering the structural and functional changes that occur at the microvascular level, at various stages after myocardial infarction (1, 3, and 7 days following damage). Towards this aim, we have adopted an interdisciplinary approach which combines confocal microscopy, fully automated 3D image analysis and mathematical modelling. We used thick cardiac tissue sections labelled for nuclei, endothelial cell junctions and smooth muscle cells and we imaged them by confocal microscopy. Firstly, we developed a novel method for the segmentation of labelled structures from confocal images as well as an innovative approach for the accurate 3D reconstruction of the microvasculature based on endothelial cell junction and smooth muscle actin staining. Subsequently, we designed a fully automated image analysis pipeline to extract parameters that quantify all major features of the microvasculature, its relation to smooth muscle actin-positive cells and capillary diffusion regions. The novel pipeline was applied to the analysis of the coronary microvasculature from healthy tissue and also tissue at various stages after myocardial infarction. We used the pig animal model, whose coronary vasculature closely resembles that of human tissue. We discovered alterations in the microvasculature, particularly structural changes and angioadaptation resulting in altered capacity for oxygen diffusion in the aftermath of myocardial infarction. In addition, we evaluated the extracted knowledge’s potential in terms of predicting the pathophysiological condition of the tissue. The high accuracy achieved in this respect, demonstrates the ability of our approach not only to quantify and identify pathology-related changes of microvascular beds, but also to predict complex and dynamic microvascular patterns. Lastly, the anatomical data obtained regarding the microvasculature were used to feed a continuum perfusion model in order to calculate physiologically meaningful permeability tensors. By using this theoretical blood flow modelling approach, we were able to obtain insights into tissue perfusion and to demonstrate the functional effect of the structural changes occurring as a result of myocardial infarction. Overall, this work is a step forward towards increasing our understanding of microvascular alterations after myocardial infarction, modelling microcirculation at different stages after tissue damage and it also provides an unbiased means for the evaluation of potential treatments

Image analysis and modelling of the infarcted heart response at the microvascular level

133 p.-37 fig.-7 tab.[EN]The coronary microvasculature comprises the smallest blood vessels of the cardiac tissue. Itcontinuously adapts in response to physiological and pathophysiological conditions to meettissue demands. Quantitative assessment of the dynamic changes taking place in the coronarymicrovasculature is therefore crucial in enhancing our knowledge regarding the impactof cardiovascular disease on tissue perfusion and o developing efficient angiotherapies.This thesis focuses on deciphering the structural and functional changes that occur at themicrovascular level, at various stages after myocardial infarction (1, 3, and 7 days following damage). Towards this aim, we have adopted an interdisciplinary approach which combines confocal microscopy, fully automated 3D image analysis and mathematical modelling. We used thick cardiac tissue sections labelled for nuclei, endothelial cell junctions and smooth muscle cells and we imaged them by confocal microscopy. Firstly, we developed a novel method for the segmentation of labelled structures from confocal images as well as an innovative approach for the accurate 3D reconstruction of the microvasculature based on endothelial cell junction and smooth muscle actin staining. Subsequently, we designed a fully automated image analysis pipeline to extract parameters that quantify all major features of the microvasculature, its relation to smooth muscle actin-positive cells and capillary diffusion regions. The novel pipeline was applied to the analysis of the coronary microvascu-lature from healthy tissue and also tissue at various stages after myocardial infarction. We used the pig animal model, whose coronary vasculature closely resembles that of human tissue. We discovered alterations in the microvasculature, particularly structural changes and angioadaptation resulting in altered capacity for oxygen diffusion in the aftermath of myocardial infarction. In addition, we evaluated the extracted knowledge’s potential in terms of predicting the pathophysiological condition of the tissue. The high accuracy achieved in this respect, demonstrates the ability of our approach not only to quantify and identify pathology-related changes of microvascular beds, but also to predict complex and dynamic microvascular patterns. Lastly, the anatomical data obtained regarding the microvasculature were used to feed a continuum perfusion model in order to calculate physiologically mean-ingful permeability tensors. By using this theoretical blood flow modelling approach, we were able to obtain insights into tissue perfusion and to demonstrate the functional effect of the structural changes occurring as a result of myocardial infarction. Overall, this work is a step forward towards increasing our understanding of microvascular alterations after myocardial infarction, modelling microcirculation at different stages after tissue damage and it also provides an unbiased means for the evaluation of potential treatments.[ES]La microvasculatura coronaria comprende los vasos sanguíneos más pequeños del tejido cardíaco. Se adapta continuamente en respuesta a las condiciones fisiológicas y fisiopatológicas para satisfacer las demandas de los tejidos. La evaluación cuantitativa de los cambios dinámicos que tienen lugar en la microvasculatura coronaria es, por lo tanto, crucial para mejorar nuestro conocimiento sobre el impacto de las enfermedades cardiovasculares en la perfusión tisular y en el desarrollo de angioterapias eficientes. Esta tesis se centra en descifrar los cambios estructurales y funcionales que ocurren a nivel microvascular, en varias etapas después del infarto de miocardio (1, 3 y 7 días después del daño). Con este objetivo, hemos adoptado un enfoque interdisciplinario que combina la microscopía confocal, el análisis de imágenes 3D totalmente automatizado y el modelado matemático. Utilizamos secciones gruesas de tejido cardíaco teñidas para núcleos, uniones de células endoteliales y células de músculo liso vascular y obtuvimos imágenes mediante microscopía confocal. En primer lugar, desarrollamos un método novedoso para la segmentación de estructuras marcadas a partir de imágenes confocales, así como un enfoque innovador para la reconstrucción 3D precisa de la microvasculatura basada en la unión de células endoteliales y la tinción de actina de músculo liso. Posteriormente, diseñamos una pipeline de análisis de imágenes completamente automatizada para extraer parámetros que cuantifican todas las características principales de la microvasculatura, su relación con las células de músculo liso que expresan actina y las regiones de difusión capilar. La nueva pipeline se aplicó al análisis de la microvasculatura coronaria de tejido sano y también de tejido en diversas etapas después del infarto de miocardio. El modelo animal utilizado fue el cerdo, cuya vasculatura coronaria se parece mucho a la humana. Descubrimos alteraciones en la microvasculatura, particularmente cambios estructurales y angioadaptación que resultan en una capacidad alterada para la difusión de oxígeno después del infarto de miocardio. Además, evaluamos el potencial del conocimiento extraído en términos de predecir la condición fisiopatológica del tejido. La alta precisión lograda en este sentido, demuestra la capacidad de nuestro abordaje no solo para cuantificar e identificar cambios patológicos de lechos microvasculares, sino también para predecir patrones microvasculares complejos y dinámicos. Por último, los datos anatómicos obtenidos para la microvasculatura se usaron para ¨alimentar¨ un modelo de perfusión continuo con el fin de calcular tensores de permeabilidad fisiológicamente significativos. Al utilizar este enfoque teórico de modelado de flujo sanguíneo, pudimos obtener información sobre la perfusión tisular y demostrar el efecto funcional de los cambios estructurales que ocurren como resultado del infarto de miocardio. En general, este trabajo es un paso adelante para aumentar nuestra comprensión de lasalteraciones microvasculares después del infarto de miocardio, modelar la microcirculación en diferentes etapas después del daño tisular y también proporciona un método imparcial para la evaluación de posibles tratamientos.Peer reviewe