A Symmetric Multiscale Detail-Guided Attention Network for Cardiac MR Image Semantic Segmentation

In the field of image object detection and semantic segmentation, improving the accuracy of object identification and segmentation is a primary goal. To achieve this, leveraging the potential of multi-scale information through feature map refinement and fusion has been widely recognized. However, existing feature fusion methods either design more complex feature pyramid networks, replace existing detectors, or incrementally introduce feature fusion modules, overlooking the effective approach of enhancing spatial information in deep feature maps. We propose a novel pluggable feature fusion paradigm termed ‘Effective Learning Bridge’. Our research introduces an efficient and adaptive learning mechanism that builds learning bridges between feature maps at different scales within the feature pyramid, thereby enhancing the spatial information of objects in deep feature maps. This mechanism is specifically designed for multi-scale feature maps and can be seamlessly integrated into any network incorporating feature maps. By altering the model’s backpropagation path, we successfully improve learning efficiency, which in turn enhances the accuracy of object detection and segmentation. Our proposed paradigm and method were extensively evaluated through experiments on SIMD, HRSID, and WHDLD datasets and benchmark models. The results unequivocally demonstrate the effectiveness of our approach in significantly improving the accuracy of object detection and semantic segmentation, as well as the overall learning efficiency of the model.

Is MRI the Preferred Method for Evaluating Right Ventricular Size and Function in Patients With Congenital Heart Disease?

Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to segment the left ventricle from short axis cine cardiac MR images automatically. Our method incorporates a number of image processing and analysis techniques including thresholding, edge detection, mathematical morphology, and image filtering to build an efficient process flow. This process flow makes use of various features in cardiac MR images to achieve high accurate segmentation results. Our method was tested on 45 clinical short axis cine cardiac images and the results are compared with manual delineated ground truth (average perpendicular distance of contours near 2 mm and mean myocardium mass overlapping over 90%). This approach provides cardiac radiologists a practical method for an accurate segmentation of the left ventricle.

The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this paper, we propose a novel multiresolution mutual assistance network (MMA-Net) for cardiac MR images segmentation. It is mainly composed of multibranch input module, multiresolution mutual assistance module, and multilabel deep supervision. First, the multibranch input module helps the network to extract local and global features more pertinently. Then, the multiresolution mutual assistance module implements multiresolution feature interaction and progressively improves semantic features to more completely express the information of the tissue. Finally, the multilabel deep supervision is proposed to generate the final segmentation map. We compare with state-of-the-art medical image segmentation methods on the medical image computing and computer-assisted intervention (MICCAI) automated cardiac diagnosis challenge datasets and the MICCAI atrial segmentation challenge datasets. The mean dice scores of our method in the left atrium, right ventricle, myocardium, and left ventricle are 0.919, 0.920, 0.881, and 0.960, respectively. The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in cardiac magnetic resonance images segmentation.

We describe the method for segmentation of Left Ventricle (LV) in short axis cardiac MR Images in order to visibly identify the LV, the endocardium and the epicardium. The interpretation and evaluation of images obtained from Magnetic Resonance Imaging (MRI) is not a trivial task and also it is very time- consuming if done completely manually. This is especially the case for higher dimensional image data such as volumetric and/or time-dependent data such as the Cardiac Images. The objective of this study was show clearly the LV in particular so that any deviation from the standard dimensions in terms of shape, size or texture, can be unmistakably identified. Early diagnosis of heart abnormalities can potentially decrease the risk of heart attacks as well as benefit people who have just undergone Heart Transplant Surgery. For segmentation of the Cardiac MRI, Principal Component Analysis (PCA) is used in the Active Appearance Model (AAM) building process. The extraction of statistical features with PCA can be regarded as a projection of high-dimensional data vectors into a space of much lower dimensionality. The process of building a computerized model is equated with making the computer learn possible appearances of an object. The basis for such a learning process is the training set which is prepared from the data obtained from a reputed hospital in Pune. I. Introduction According to the Organ Procurement and Transplantation Network (OPTN), out of approximately 4000 heart transplants performed every year in the United States, 543 patients die in the first year, only 78% survive 3 years after the transplant and 72% after 5 years. Most of these deaths are due to rejection of the transplanted organs. To prevent rejection, patients are given immuno-suppressants, which suppress the immune system to enable acceptance of the newly transplanted organ. However, excess of these drugs destroy the patient's immune system making them susceptible to numerous diseases. These patients are monitored closely by doctors to adjust the dosage of immuno-suppressants given to them. One method of doing this is by biopsy, which involves taking a small piece of the heart muscle and inspecting it under the microscope for damaged cells. This procedure is highly invasive and prone to sampling errors. MRI can be used to effectively monitor any functional abnormality of the heart in a non-invasive manner. Segmentation of the cardiac MRI images into epicardium and ventricles helps to detect any deformities and thus could indicate the on-setting of rejection. This segmentation can be performed manually by experts, but it is a time consuming process, and may delay the detection of rejection at a crucial stage. The manually performed segmentation also suffers the problem of inconsistency. Hence, efforts have been made to make this segmentation procedure completely automatic. (1, 2) The heart is a muscular organ of the circulatory system that constantly pumps blood throughout the body. As seen in figure 1, the heart has four separate chambers. The upper chambers are called the left and right atria, and the lower chambers are called the left and right ventricles. The septum separates the right and left sides of the heart. The endocardium is the inner surface of the myocardium (the heart muscle tissue) and the epicardium is the outer surface of the myocardium. The muscular tissues that attach to the lower portion of the interior wall of the ventricles are the papillary muscles. The periodic motion of the walls of the heart chambers during one heartbeat is referred to as one cardiac cycle. One heartbeat consists of two phases, namely systole and diastole. The rhythmic contraction of the ventricles, by which blood is driven through the aorta and pulmonary artery is the systole. Diastole is the normal rhythmically occurring relaxation and dilatation of the ventricles, during which they fill with blood. The right ventricle delivers blood to the lungs. The left ventricle delivers blood to the rest of the body. So the left ventricular wall is thicker. This fact is often used for distinguishing between the left and right ventricles. Here, we study the MRI images of the transverse section of the heart and segment them into epicardium, right ventricle, and left ventricle. (1, 2) Magnetic Resonance Imaging (MRI) is a method of creating images of the inside of opaque organs in living organisms. When the object to be imaged is placed in a powerful uniform magnetic field, the spins of the hydrogen nuclei with non-zero spin numbers, within the tissue, all align in one of two opposite directions: parallel to the magnetic field or three orthogonal image gradients are applied, to selectively image the different voxels (3-D pixels). They are the slice selection gradient, the phase encoding gradient, and the frequency

This paper presents a novel edge following technique for image segmentation designed to segment the left ventricle in cardiac magnetic resonance (MR) images. This is a required step to determine the volume of left ventricle in a cardiac MR image, which is an essential tool for cardiac diagnosis. The traditional method for extracting them from cardiac MR images is by human delineation. This method is accuracy but time consuming. So a new ventricular segmentation technique is proposed in order to reduce the analysis time with similar accuracy level compared to doctorspsila opinions. Our proposed technique can detect ventricle edges in MR images using the information from the vector image model and the edge map. We also compare the proposed segmentation technique with the active contour model (ACM) and the gradient vector flow (GVF) by using the opinions of two skilled doctors as the ground truth. The experimental results show that our technique is able to provide more accurate segmentation results than the classical contour models and visually close to the manual segmentation by the experts. The results evaluated using a numerical measure by mean of the probability of error in image segmentation also confirm the visual evaluation.

With the rapid development of deep convolutional neural networks, the results of image semantic segmentation are remarkable, and the segmentation effect is greatly improved. The pooling layer of the convolutional neural network will reduce the resolution of the feature map, which makes the convolutional neural network lose a lot of spatial information while extracting semantic features. How to integrate semantic features with semantic information and spatial information will become an important factor to improve the performance of semantic segmentation. Firstly, this paper improves the global attention upsampling module and uses the improved global attention upsampling module to form a multiscale global attention up-mining module in a new connection way. The upsampling module of multiscale attention establishes the relationship between high-level features and lower-level features at a longer distance. Compared with PANet, the method proposed in this paper deepens the close relationship between semantic information and spatial information. Experiments show that the segmentation effect of the feature fusion method based on cascade is better than that of the feature fusion method based on weight. The segmentation effect of the two fusion methods is improved by 8.3% and 5.7% compared with the PANet on the PASCAL VOC 2012 dataset and by 4.5% and 3.6% on the Cityscapes dataset, respectively. The research results of this paper make the high-level semantic information and shallow feature information cooperate to improve the segmentation effect.

Purpose To determine the incidence of ventricular fatty replacement and late gadolinium enhancement (LGE) at cardiac magnetic resonance (MR) imaging in patients with arrhythmogenic right ventricular (RV) dysplasia/cardiomyopathy (ARVD/C) and the relationship of these findings to disease severity. Materials and Methods This was a retrospective institutional review board-approved HIPAA-compliant study. All subjects provided written informed consent. Seventy-six patients with ARVD/C were enrolled from 2002 to 2012. Quantitative and qualitative cardiac MR imaging analyses of the RV and the left ventricle (LV) were performed to determine cardiac MR imaging-specific Task Force Criteria (TFC) and non-TFC features (ARVD/C-type pattern of fatty infiltration and/or nonischemic pattern LGE). Patients were separated into four groups on the basis of cardiac MR imaging TFC: (a) patients with major cardiac MR imaging criteria, (b) patients with minor criteria, (c) patients with partial criteria, and (d) patients with no criterion. Continuous variables were compared by using the independent Student t test and analysis of variance. Categoric variables were compared by using the Fisher exact test. Results Of 76 patients (mean age, 34.2 years ± 14 [standard deviation]; 51.3% men), 42 met major cardiac MR imaging criteria, seven met minor criteria, seven met partial criteria, and 20 met no criterion. Most probands (36 [80.0%] of 45) met major or minor cardiac MR imaging criteria. Only 13 (41.9%) of 31 family members met any cardiac MR imaging criterion. The most common non-TFC MR imaging features were RV fatty infiltration (28.9%) and LV LGE (35.5%). Non-TFC cardiac MR imaging features were seen in 88.1% of subjects with major criteria, in 28.6% of those with minor criteria, in 71.4% of those with partial criteria, and in 10.0% of those with no criteria. Conclusion In this large cohort of patients with ARVD/C, non-TFC findings of ventricular fatty infiltration and LGE were frequent and were most often found in those who met major cardiac MR imaging criteria and in probands. (©) RSNA, 2016 Online supplemental material is available for this article.

Major advances in the field of pediatric cardiology and cardiac surgery over the last several decades have led to a dramatic improvement in survival rates for most forms of congenital heart disease (CHD). For example, hypoplastic left heart syndrome, a previously lethal defect, now has early survival rates up to 90% at major centers.1 These improved outcomes have produced a growing population of survivors with complex CHD who are now reaching adulthood (Figure 1). During this period, improvements in surgical and medical treatments have been accompanied by developments in diagnostic modalities. Echocardiography has replaced catheterization as the primary diagnostic modality, and it is now uncommon for newborn infants to undergo catheterization for purely diagnostic purposes. Although echocardiography remains the bedrock of noninvasive cardiac imaging, the array of diagnostic modalities and techniques available continue to grow and this has spawned the specialty of “noninvasive cardiac imaging” and the need for the “cardiac imager” to be adept in all the different modalities. Figure 1. Percentage of patients under the age of 1 year (grey bars) and over the age of 18 years (black bars) undergoing echocardiography at Children’s Hospital Boston from 1983 through 2006. Note the reverse trends of these age groups reflecting the steady increase in the proportion of adult patients with congenital heart disease. Although the absolute number of infants undergoing echocardiography during this time period has increased, their proportion has steadily declined. Echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography (CCT) are the primary modalities used for noninvasive cardiac imaging in patients with CHD. Nuclear scintigraphy is used in selected circumstances. The Table summarizes the strengths and weaknesses of each modality. Figure 2 shows temporal trends in utilization for the various noninvasive cardiac imaging techniques at our center. It is clear that echocardiography is the most frequently …

In the field of computer vision, the detection of multiple objects with different scales within a single image is challenging. To target this problem, feature pyramids are a basic component commonly found in multi-scale object detectors. In the construction of standard feature pyramids, different semantic features are simply connected to rebuild a new feature map, regardless of whether these features have a positive effect to the output or not. In order to avoid introducing too many redundant features within the feature fusion stage, a new feature fusion module called the Feature Selection Module (FSM) was proposed in this paper, which can automatically detect the most representative features for the rebuilding of feature maps. The channel attention mechanism in FSM is able to process and score each channel, filtering out irrelevant features while focusing on features with high contribution. Moreover, FSM can be easily embedded within feature pyramids. Simply adding a small number of trainable parameters to the network can significantly improve the ability of feature extraction. We validated our FSM with the VOC 2007 object detection dataset, based on Yolo series detectors. Findings from the present study demonstrates that for a small computational cost, our method is able to consistently improve the performance of Yolo detectors.

Left ventricle (LV) segmentation of cardiac magnetic resonance (MR) images is essential for evaluating cardiac function parameters and diagnosing cardiovascular diseases (CVDs). Accurate LV segmentation remains a challenge because of the large differences in cardiac structures in different research subjects. In this work, a network based on an encoder–decoder architecture for automatic LV segmentation of short-axis cardiac MR images is proposed. It combines UNet 3+ and Transformer to jointly predict the segmentation masks and signed distance maps (SDM). UNet 3+ can extract coarse-grained semantics and fine-grained details from full scales, while a Transformer is used to extract global features from cardiac MR images. It solves the problem of low segmentation accuracy caused by blurred LV edge information. Meanwhile, the SDM provides a shape-aware representation for segmentation. The performance of the proposed network is validated on the 2018 MICCAI Left Ventricle Segmentation Challenge dataset. The five-fold cross-validation evaluation was performed on 145 clinical subjects, and the average dice metric, Jaccard coefficient, accuracy, and positive predictive value reached 0.908, 0.834, 0.979, and 0.903, respectively, showing a better performance than that of other mainstream ones.

Clinical indications of heart disease are shown from left ventricle (LV) or right ventricle (RV) volume measurements of cardiac MRI images. LV and RV segmentation of cardiac MRI images can detect and measure image volume. Public dataset MICCAI, ACDC, Kaggle, and SCD provide data on MRI images of cardiac that have been widely used by researchers. The deep learning method approach can optimally solve problems in analyzing heart disease from cardiac MRI images. The aim of this paper is to determine the availability of public datasets that are appropriate for the research objectives. It can support the optimization of the segmentation method for LV and RV images of cardiac as the contribution of this paper. The results of the study are that the public dataset (MICCAI, ACDC, Kaggle, and SCD) provides sufficient data for the identification, classification, and measurement of LV and RV volumes. Furthermore, a deep learning approach with convolutional neural networks can detect and classify heart diseases with high accuracy.

Traditionally, cardiac image analysis is done manually. Automatic image processing can help with the repetitive tasks, and also deal with huge amounts of data, a task which would be humanly tedious. This study aims to develop a spectrum-based computer-aided tool to locate the left ventricle using images obtained via cardiac magnetic resonance imaging. Discrete Fourier Transform was conducted pixelwise on the image sequence. Harmonic images of all frequencies were analyzed visually and quantitatively to determine different patterns of the left and right ventricles on spectrum. The first and fifth harmonic images were selected to perform an anisotropic weighted circle Hough detection. This tool was then tested in ten volunteers. Our tool was able to locate the left ventricle in all cases and had a significantly higher cropping ratio of 0.165 than did earlier studies. In conclusion, a new spectrum-based computer aided tool has been proposed and developed for automatic left ventricle localization. The development of this technique, which will enable the automatic location and further segmentation of the left ventricle, will have a significant impact in research and in diagnostic settings. We envisage that this automated method could be used by radiographers and cardiologists to diagnose and assess ventricular function in patients with diverse heart diseases.

Semantic segmentation of cardiac MR images is a challenging task due to its importance in medical assessment of heart diseases. Having a detailed localization of specific regions of interest such as Right and Left Ventricular Cavities and Myocardium, doctors can infer important information about the presence of cardiovascular diseases, which are today a major cause of death globally. This paper addresses the problem of semantic segmentation in cardiac MR images using a dilated Convolutional Neural Network. Opting for dilated convolutions allowed us to work in full resolution throughout the network's layers, preserving localization accuracy, while maintaining a relatively small number of trainable parameters. To assist the network's training process we designed a custom loss function. Furthermore, we developed new augmentation techniques and also adapted existing ones, to cope for the lack of sufficient training images. Consequently, the training set increases not only by amount, but by substance as well, and the network trains quickly and efficiently without overfitting. Our pre- and post-processing steps are also crucial to the whole process. We apply our methodology for the Right and Left Ventricles (RV, LV) and also the Myocardium (MYO) according to the Automated Cardiac Diagnosis Challenge (ACDC) with promising results. Submitting our algorithm's predictions to the Post-2017-MICCAI-challenge testing phase, we achieved similar scores (average Dice coefficient 0.916) on the test data set compared to the state of the art featured in the ACDC leaderboard, but with significantly fewer parameters than the leading method. Our approach outperforms other methods featuring dilated convolutions in this challenge up until now.

The characterization of cardiac function is of high clinical interest for early diagnosis and better patient follow-up in cardiovascular diseases. A large number of cardiac image analysis methods and more precisely in cine-magnetic resonance imaging (MRI) have been proposed to quantify both shape and motion parameters. However, the first major problem to address lies in the cardiac image segmentation that is most often needed to extract the myocardium before any other process such as motion tracking, or registration. Moreover, intelligent systems based on classification and learning techniques have emerged over the last years in medical imaging. In this chapter we focus in the use of sparse representation and dictionary learning (DL) in order to get insights about the diseased heart in the context of cardiovascular diseases (CVDs). Specifically, this work focuses on fibrosis detection in patients with hypertrophic cardiomyopathy (HCM).