Ventricle Segmentation Research Articles

Misdiagnosed takotsubo syndrome: a case report

Takotsubo syndrome (TTS) is characterized by acute and transient dysfunction of the apical segment of the left ventricle. In recent years, there have been increasing numbers of case reports about TTS; however, it is still being neglected and misdiagnosed in many primary hospitals in China. We present a case of a 68-year-old female who presented to our hospital with one month of paroxysmal cough. Here severe cough would sometimes induce headache, chest pain, nausea, and vomiting. She had an unexplained cardiogenic shock approximately 4 months prior and gradually developed orthopnea. Cardiac biomarkers were mildly elevated, and electrocardiogram (ECG) displayed diffuse and deep T-waves inversion in leads I, II, AVL, and V2-V9, like acute myocardial infarction. However, coronary angiography was performed and showed the absence of obstructed coronary atherosclerosis or acute plaque rupture. The patient was successively treated in four hospitals and was eventually diagnosed with TTS in our hospital (the fourth hospital) due to noncardiogenic discomfort. Physicians at some primary hospitals require additional clinical experience to deeply understand TTS. Many doctors could learn about TTS from a medical textbook but remain unfamiliar with the disease. We hope that through analysis of this case, primary doctors will have a deeper understanding of TTS, avoid misdiagnosing the typical cases that occur in their patients.

Automated Peak Prominence-Based Iterative Dijkstra's Algorithm for Segmentation of B-Mode Echocardiograms.

We present a user-initialized, automated left ventricle (LV) segmentation method for use with echocardiograms (echo). The method uses an iterative Dijkstra's algorithm, strategic node selection, and novel cost matrix formulation based on intensity peak prominence and is termed the "Prominence Iterative Dijkstra's" algorithm, or ProID. ProID is initialized with three user-input clicks per time-series scan. ProID was tested using artificial echos representing five different systems. Results showed accurate LV contours and volume estimations as compared to the ground-truth for all systems. Using the CAMUS dataset, we demonstrate ProID maintained similar Dice similarity scores (DSS) to other automated methods. ProID was then used to analyze a clinical cohort of 66 pediatric patients, including normal and diseased hearts. Output segmentations, LV volume, and ejection fraction were compared against manual segmentations from two expert readers. ProID maintained an average DSS of 0.93 when comparing against manual segmentation. Comparing the two expert readers, the manual segmentations maintained a DSS of 0.93 which increased to 0.95 when they used ProID. Thus, ProID reduced inter-operator variability across the expert readers. Overall, this work demonstrates ProID yields accurate boundaries across age groups, disease states, and echo platforms with low computational cost and no need for training data.

BIVENTRICULAR NON-COMPACTION CARDIOMYOPATHY WITH LEFT VENTRICULAR DYSFUNCTION

Non-compaction of the ventricular myocardium involving both ventricles is a rare condition and characterized by deeply increased trabeculations in one or more segments of the ventricle. Non-compaction is thought to be due to intrauterine arrest of compaction of the loosely interwoven meshwork present in fetal myocardial primordium. More recent studies suggest that non-compaction of the ventricular myocardium can be acquired later on in life sporadically.The apical segment of the left ventricle is most commonly affected, but biventricular involvement is also described. Herein, we describe an unusual case of biventricular non-compaction cardiomyopathy with left ventricular systolic and diastolic dysfunction. A 54-year-old man presented with clinical heart failure as well as arrhythmia. Transthoracic echocardiography revealed left ventricular non-compaction with depressed ejection fraction. Cardiac magnetic resonance imaging (MRI) further revealed right ventricular noncompaction.

Evaluating Usefulness of Deep Learning Based Left Ventricle Segmentation in Cardiac Gated Blood Pool Scan

Evaluating Usefulness of Deep Learning Based Left Ventricle Segmentation in Cardiac Gated Blood Pool Scan

Automatic cardiac evaluations using a deep video object segmentation network

BackgroundAccurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network.ResultsThe proposed method accurately segments the left and right ventricle regions in four-chamber view echocardiography series with a dice similarity coefficient of 94.03% and 94.97%, respectively. Further post-processing procedures on the segmented left and right ventricle regions resulted in a mean absolute error of 3.13% and 2.03% for ejection fraction and fractional area change parameters, respectively.ConclusionThis study has achieved excellent performance on the left and right ventricle segmentation, leading to more accurate estimations of vital cardiac parameters such as ejection fraction and fractional area change parameters in the left and right ventricle functionalities, respectively. The results represent that our method can predict an assured, accurate, and reliable cardiac function diagnosis in clinical screenings.

Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation.

BackgroundArtificial intelligence (AI) techniques have been proposed for automation of cine CMR segmentation for functional quantification. However, in other applications AI models have been shown to have potential for sex and/or racial bias. The objective of this paper is to perform the first analysis of sex/racial bias in AI-based cine CMR segmentation using a large-scale database.MethodsA state-of-the-art deep learning (DL) model was used for automatic segmentation of both ventricles and the myocardium from cine short-axis CMR. The dataset consisted of end-diastole and end-systole short-axis cine CMR images of 5,903 subjects from the UK Biobank database (61.5 ± 7.1 years, 52% male, 81% white). To assess sex and racial bias, we compared Dice scores and errors in measurements of biventricular volumes and function between patients grouped by race and sex. To investigate whether segmentation bias could be explained by potential confounders, a multivariate linear regression and ANCOVA were performed.ResultsResults on the overall population showed an excellent agreement between the manual and automatic segmentations. We found statistically significant differences in Dice scores between races (white ∼94% vs. minority ethnic groups 86–89%) as well as in absolute/relative errors in volumetric and functional measures, showing that the AI model was biased against minority racial groups, even after correction for possible confounders. The results of a multivariate linear regression analysis showed that no covariate could explain the Dice score bias between racial groups. However, for the Mixed and Black race groups, sex showed a weak positive association with the Dice score. The results of an ANCOVA analysis showed that race was the main factor that can explain the overall difference in Dice scores between racial groups.ConclusionWe have shown that racial bias can exist in DL-based cine CMR segmentation models when training with a database that is sex-balanced but not race-balanced such as the UK Biobank.

Learning to complete incomplete hearts for population analysis of cardiac MR images.

Cardiac MR acquisition with complete coverage from base to apex is required to ensure accurate subsequent analyses, such as volumetric and functional measurements. However, this requirement cannot be guaranteed when acquiring images in the presence of motion induced by cardiac muscle contraction and respiration. To address this problem, we propose an effective two-stage pipeline for detecting and synthesising absent slices in both the apical and basal region. The detection model comprises several dense blocks containing convolutional long short-term memory (ConvLSTM) layers, to leverage through-plane contextual and sequential ordering information of slices in cine MR data and achieve reliable classification results. The imputation network is based on a dedicated conditional generative adversarial network (GAN) that helps retain key visual cues and fine structural details in the synthesised image slices. The proposed network can infer multiple missing slices that are anatomically plausible and lead to improved accuracy of subsequent analyses on cardiac MRIs, e.g., ventricle segmentation, cardiac quantification compared to those derived from incomplete cardiac MR datasets. For instance, the results obtained when compensating for the absence of two basal slices show that the mean differences to the reference of stroke volume and ejection fraction are only -1.3mL and -1.0%, respectively, which are significantly smaller than those calculated from the incomplete data (-26.8mL and -6.7%). The proposed approach can improve the reliability of high-throughput image analysis in large-scale population studies, minimising the need for re-scanning patients or discarding incomplete acquisitions.

A U-snake based deep learning network for right ventricle segmentation.

Ventricular segmentation is of great importance for the heart condition monitoring. However, manual segmentation is time-consuming, cumbersome, and subjective. Many segmentation methods perform poorly due to the complex structure and uncertain shape of the right ventricle, so we combine deep learning to achieve automatic segmentation. This paper proposed a method named U-Snake network which is based on the improvement of deep snake together with level set to segment the right ventricular in the MR images. U-snake aggregates the information of each receptive field which is learned by circular convolution of multiple dilation rates. At the same time, we also added dice loss functions and transferred the result of U-Snake to the level set so as to further enhance the effect of small object segmentation. Our method is tested on the test 1 and test 2 datasets in the right ventricular segmentation challenge (RVSC), which shows the effectiveness. The experiment showed that we have obtained good result in the RVSC. The highest segmentation accuracy on the right ventricular test set 2 reached a dice coefficient of 0.911, and the segmentation speed reached 5 fps. Our method, a new deep learning network named U-snake, has surpassed the previous excellent ventricular segmentation method based on mathematical theory and other classical deep learning methods, such as Residual U-net, Inception CNN, and Dilated CNN. However, it can only be used as an auxiliary tool instead of replacing the work of human beings.

AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network.

Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graph, deformable model, and artificial intelligence methods. This article presents a systematic mapping on left ventricle segmentation, considering 74 studies published in the past decade. The main contributions of this review are definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.

Automated 3D U‐net based segmentation of neonatal cerebral ventricles from 3D ultrasound images

Intraventricular hemorrhaging (IVH) within cerebral lateral ventricles affects 20-30% of very low birth weight infants (<1500g). As the ventricles increase in size, the intracranial pressure increases, leading to post-hemorrhagic ventricle dilatation (PHVD), an abnormal enlargement of the head. The most widely used imaging tool for measuring IVH and PHVD is cranial two-dimensional (2D) ultrasound (US). Estimating volumetric changes over time with 2D US is unreliable due to high user variability when locating the same anatomical location at different scanning sessions. Compared to 2D US, three-dimensional (3D) US is more sensitive to volumetric changes in the ventricles and does not suffer from variability in slice acquisition. However, 3D US images require segmentation of the ventricular surface, which is tedious and time-consuming when done manually. A fast, automated ventricle segmentation method for 3D US would provide quantitative information in a timely manner when monitoring IVH and PHVD in pre-term neonates. To this end, we developed a fast and fully automated segmentation method to segment neonatal cerebral lateral ventricles from 3D US images using deep learning. Our method consists of a 3D U-Net ensemble model composed of three U-Net variants, each highlighting various aspects of the segmentation task such as the shape and boundary of the ventricles. The ensemble is made of a U-Net++, attention U-Net, and U-Net with a deep learning-based shape prior combined using a mean voting strategy. We used a dataset consisting of 190 3D US images, which was separated into two subsets, one set of 87 images contained both ventricles, and one set of 103 images contained only one ventricle (caused by limited field-of-view during acquisition). We conducted fivefold cross-validation to evaluate the performance of the models on a larger amount of test data; 165 test images of which 75 have two ventricles (two-ventricle images) and 90 have one ventricle (one-ventricle images). We compared these results to each stand-alone model and to previous works including, 2D multiplane U-Net and 2D SegNet models. Using fivefold cross-validation, the ensemble method reported a Dice similarity coefficient (DSC) of 0.720 ± 0.074, absolute volumetric difference (VD) of 3.7 ± 4.1 cm3 , and a mean absolute surface distance (MAD) of 1.14 ± 0.41mm on 75 two-ventricle test images. Using 90 test images with a single ventricle, the model after cross-validation reported DSC, VD, and MAD values of 0.806 ± 0.111, 3.5 ± 2.9 cm3 , and 1.37 ± 1.70mm, respectively. Compared to alternatives, the proposed ensemble yielded a higher accuracy in segmentation on both test data sets. Our method required approximately 5 s to segment one image and was substantially faster than the state-of-the-art conventional methods. Compared to the state-of-the-art non-deep learning methods, our method based on deep learning was more efficient in segmenting neonatal cerebral lateral ventricles from 3D US images with comparable or better DSC, VD, and MAD performance. Our dataset was the largest to date (190 images) for this segmentation problem and the first to segment images that show only one lateral cerebral ventricle.

Fully automatic segmentation of LV from echocardiography images and calculation of ejection fraction using deep learning

Echocardiography is a widely used ultrasound imaging technique for cardiac health diagnosis. Echocardiography segmentation is a crucial process to evaluate multiple cardiac parameters like ejection fraction, heart wall thicknesses, etc. Recently machine learning techniques especially deep learning using convolution neural network models are finding increasing applications for echo image analysis including its segmentation. In this paper, we have presented a unique convolution neural network (CNN) model for automatic left ventricle (LV) segmentation of echo images. Denoising and feature extraction processes are integrated with the CNN model to enhance its prediction accuracies after training. The proposed system is trained on two-dimensional sequence images of 70 patients and tested on data of 12 patients. An automatic method for evaluation of ejection fraction is appended using the LV segmentation predictions generated by the CNN model. The performance of this CNN architecture is evaluated with recent architectures using various similarities and distance-based majors as well as ejection fraction correlation with ground truth segmentation labelled images. CNN layer visualisation methods are applied to obtain deeper insight into the trained network.

A Motion Aware Dnn Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine Mr Sequences

A Motion Aware Dnn Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine Mr Sequences

Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography-Performance Study In Silico.

Although the two dimensional Speckle Tracking Echocardiography has gained a strong position among medical diagnostic techniques in cardiology, it still requires further developments to improve its repeatability and reliability. Few works have attempted to incorporate the left ventricle segmentation results in the process of displacements and strain estimation to improve its performance. We proposed the use of mask information as an additional penalty in the elastic image registration based displacements estimation. This approach was studied using a short axis view synthetic echocardiographic data, segmented using an active contour method. The obtained masks were distorted to a different degree, using different methods to assess the influence of the segmentation quality on the displacements and strain estimation process. The results of displacements and circumferential strain estimations show, that even though the method is dependent on the mask quality, the potential loss in accuracy due to the poor segmentation quality is much lower than the potential accuracy gain in cases where the segmentation performs well.

MFAUNet: Multiscale feature attentive U‐Net for cardiac MRI structural segmentation

The accurate and robust automatic segmentation of cardiac structures in magnetic resonance imaging (MRI) is significant in calculating cardiac clinical functional indices, and diagnosing heart diseases. Most U-Net based methods use pooling, transposed convolution, and skip connection operations to integrate the multiscale features for improved segmentation in cardiac MRI. However, this architecture lacks adequate semantic connection between the channel and spatial information, and robustness in segmenting objects with significant shape variations. In this paper, a new multiscale feature attentive U-Net for cardiac MRI structural segmentation method is proposed. An attention mechanism is adopted after concatenating the multi-level features to aggregate different scale features and determine on which features to focus. Cascade and parallel dilated convolution is also employed in the decoder blocks and skip connection is employed to enhance the ability of sensing receptive fields for multiscale context information. Furthermore, deep supervision approach with a loss function that combines the dice and cross-entropy losses to reduce overfitting and ensure better prediction is introduced. The proposed method was evaluated on three public cardiac datasets. The experimental results indicate that the method achieved competitive segmentation performance with the three datasets, which verifies the robustness and generalisability of the proposed network. In comparison with conventional U-Net methods, the model leverages attention mechanism and dilated convolution block, which increases the semantic connection between the channel and the spatial information, and improves the robustness of the right ventricle segmentation performance. From the view of the Dice scores and segmentation results, the multiscale feature attentive U-Net method is one of effective methods in segmenting cardiac MRI structures.

Light-Weight Localization and Scale-Independent Multi-gate UNET Segmentation of Left and Right Ventricles in MRI Images.

Heart segmentation in cardiac magnetic resonance images is heavily used during the assessment of left ventricle global function. Automation of the segmentation is crucial to standardize the analysis. This study aims at developing a CNN-based framework to aid the clinical measurements of the left ventricle and right ventricle in cardiac magnetic resonance images. We propose a fully automated framework for localization and segmentation of the left ventricle and right ventricle in both short- and long-axis views from cardiac magnetic resonance images. The localization module utilizes a light-weight model that detects the region of interest and feeds it to the segmentation model. Also, we propose the Multi-Gate block as an extension to the UNet to boost the segmentation performance by aggregating multi-scale features. Comparison between our proposed method and the baseline UNet was performed to show the gain in the overall performance. The reliability of the model was assessed by testing the method against cardiac magnetic resonance images with different levels of noise and deformations. Heart localization accuracy was 0.59 and 1.75 pixels in both short- and long-axis views respectively. Left and right ventricle blood-pool segmentation Dice was (0.93, 0.90) in end-systole and (0.97, 0.95) in end-diastole. The left ventricle myocardium was segmented accurately with Dice of 0.91 and 0.90 in end-systole and end-diastole respectively. Left ventricle ejection fraction was found to be highly correlated with the gold standard with r = 0.987. Moreover, the proposed pipeline is fast, achieving 0.002 sec per image on average. Adding the Multi-Gate Dilated Inception Block has boosted the performance of UNet architecture and has shown generalization ability when tested on noisy and deformed cardiac magnetic resonance images. The proposed method has proven its wide applicability and reliability for heart detection when tested on different datasets.

Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors.

Cardiac cine-MRI is one of the most important diagnostic tools for characterizing heart-related pathologies. This imaging technique allows clinicians to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is highly dependent on the observer expertise and a high inter-reader variability is frequently observed. Alternatively, the ejection fraction, a quantitative heart dynamic measure, is used to identify potential cardiac diseases. Unfortunately, this type of measurement is insufficient to distinguish among different cardiac pathologies. This quantification does not exploit all the heart functional information conveyed by cine-MRI sequences. Automatic image analysis might help to identify visual patterns associated with cardiac diseases in the cine-MRI sequences and highlight potential biomarkers. This paper introduces a conditional generative adversarial network that learns a mapping between the latent space and a generated cine-MRI data distribution involving information from five different cardiac pathologies. This net is guided from the left ventricle segmentation and the velocity field that is computed as prior information to focus on the deep representation of salient cardiac patterns. Once the deep neural networks are trained, a set of validation cine-MRI slices is represented in the embedding space. The associated embedding descriptor, in the latent space, is found by minimizing a reconstruction error in the generator output. We evaluated the obtained embedded representation as a disease marker by using different classification models in 16000 pathological cine-MRI slices. The representation retrieved by using the best conditional generative model configuration was used on the classifier models yielding an average accuracy of 90.04% and an average F1-score of 89.97% in the classification task.Clinical relevance-Construction of a topological embedding space, from generative representation, that fully exploits hidden relationships of cine-MRI and represent cardiac diseases.

Anatomical knowledge based level set segmentation of cardiac ventricles from MRI

This paper represents a novel level set framework for segmentation of cardiac left ventricle (LV) and right ventricle (RV) from magnetic resonance images based on anatomical structures of the heart. We first propose a level set approach to recover the endocardium and epicardium of LV by using a bi-layer level set (BILLS) formulation, in which the endocardium and epicardium are represented by the 0-level set and k-level set of a level set function. Furthermore, the recovery of LV endocardium and epicardium is achieved by a level set evolution process, called convexity preserving bi-layer level set (CP-BILLS). During the CP-BILLS evolution, the 0-level set and k-level set simultaneously evolve and move toward the true endocardium and epicardium under the guidance of image information and the impact of the convexity preserving mechanism as well. To eliminate the manual selection of the k-level, we develop an algorithm for automatic selection of an optimal k-level. As a result, the obtained endocardial and epicardial contours are convex and consistent with the anatomy of cardiac ventricles. For segmentation of the whole ventricle, we extend this method to the segmentation of RV and myocardium of both left and right ventricles by using a convex shape decomposition (CSD) structure of cardiac ventricles based on anatomical knowledge. Experimental results demonstrate promising performance of our method. Compared with some traditional methods, our method exhibits superior performance in terms of segmentation accuracy and algorithm stability. Our method is comparable with the state-of-the-art deep learning-based method in terms of segmentation accuracy and algorithm stability, but our method has no need for training and the manual segmentation of the training data.

Fairness in AI: are deep learning-based CMR segmentation algorithms biased?

Abstract Background/Introduction Artificial intelligence (AI) is providing opportunities to transform cardiovascular medicine. A particular challenge in the application of AI technology is their potential for intrinsic and extrinsic biases, such as those based on gender and/or ethnicity. Unless satisfactorily addressed, these biases could lead to inequalities in early diagnosis, treatments and outcomes. Fairness in AI is a relatively new but fast-growing research field which deals with assessing and addressing potential bias in AI models. Purpose To perform the first analysis that assesses bias in AI-based cardiac MR segmentation models in a large-scale database. Methods A state-of-the-art deep learning (DL) based segmentation network, the “nnU-Net” framework [1], was used for automatic segmentation of both ventricles and the myocardium from cine short-axis cardiac MR over the full cardiac cycle. The dataset used consisted of end-diastole and end-systole short-axis cine cardiac MR images of 5,903 subjects (61.5±7.1 years). The nnU-Net network was trained and evaluated using a 5-fold cross validation (splits: train 60% / validation 20% / test 20%). Data on race and gender were obtained from the UK Biobank database and their distribution is summarized in Figure 1. To assess gender and racial bias in the segmentation network, we compared the Dice scores - which measure the overlap between manual and automatic segmentations – and the absolute error in measurements of biventricular volumes and function between patients grouped by ethnicity and gender. Results Figure 2 shows the Dice scores and the volumetric and functional measures for the full database, stratified by gender and by ethnicity. Results on the overall population showed an excellent agreement between the manual and automatic segmentations which is consistent with previous reported results [2–3]. However, we find statistically significant differences in Dice scores as well as volumetric measures between different ethnicities, showing that the segmentation network is biased against minority racial groups. No significant differences were found in Dice scores between genders. Similarly, for the end diastolic, end systolic volumes and ejection fraction, there were statistically significant differences in absolute error between the overall population and all racial groups except white. Conclusion(s) We have shown, for the first time, that racial bias exists in DL-based cardiac MR segmentation models. Our hypothesis is that this bias is a result of the unbalanced nature of the training data, and this is supported by the results which show that there is racial bias but not gender bias when trained using the UK Biobank database, which is gender-balanced but not race-balanced. In this work we want to highlight the potential issue of bias in DL-based image segmentation models when translating into a clinical environment. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): - EPSRC- Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King's College London Figure 1Figure 2