Multi-sequence Cardiac MR Research Articles

Dense-sparse representation matters: A point-based method for volumetric medical image segmentation

Deep learning methods utilizing Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in volumetric medical image analysis. While successful, the symmetrical structure of numerous networks pays insufficient attention to the encoding phase, and the large amount of memory occupied by voxels leads to unnecessary redundancy in the network. In this paper, we present a novel approach to handle volumetric medical images by converting them into point cloud and introduce a new asymmetrical segmentation architecture. We propose a dual-path encoder that fully captures both dense and sparse representations of the input point cloud sampled from volumes. Moreover, the two obtained representations are subtracted at the skip connection as a complementary feature during the decoding stage. Experimental results on the Brain Tumor Segmentation (BraTS) and the Multi-sequence Cardiac MR Segmentation tasks demonstrate the great potential of our point-based method for volumetric medical image segmentation.

EDRL: Entropy-guided disentangled representation learning for unsupervised domain adaptation in semantic segmentation

Background and ObjectiveDeep learning-based approaches are excellent at learning from large amounts of data, but can be poor at generalizing the learned knowledge to testing datasets with domain shift, i.e., when there exists distribution discrepancy between the training dataset (source domain) and the testing dataset (target domain). In this paper, we investigate unsupervised domain adaptation (UDA) techniques to train a cross-domain segmentation method which is robust to domain shift, eliminating the requirement of any annotations on the target domain. MethodsTo this end, we propose an Entropy-guided Disentangled Representation Learning, referred as EDRL, for UDA in semantic segmentation. Concretely, we synergistically integrate image alignment via disentangled representation learning with feature alignment via entropy-based adversarial learning into one network, which is trained end-to-end. We additionally introduce a dynamic feature selection mechanism via soft gating, which helps to further enhance the task-specific feature alignment. We validate the proposed method on two publicly available datasets: the CT-MR dataset and the multi-sequence cardiac MR (MS-CMR) dataset. ResultsOn both datasets, our method achieved better results than the state-of-the-art (SOTA) methods. Specifically, on the CT-MR dataset, our method achieved an average DSC of 84.8% when taking CT as the source domain and MR as the target domain, and an average DSC of 84.0% when taking MR as the source domain and CT as the target domain. ConclusionsResults from comprehensive experiments demonstrate the efficacy of the proposed EDRL model for cross-domain medical image segmentation.

DCNet: Diversity convolutional network for ventricle segmentation on short-axis cardiac magnetic resonance images

To accurately and simultaneously segment myocardium, left and right ventricles at the end-diastolic (ED) and end-systolic (ES) phases from short-axis cardiac magnetic resonance (CMR) images with inherent variability in appearance, shape, and location of the region of interest (ROI), we propose a diversity convolutional network (DCNet) that aims to solve ventricle under- and over-segmentation problems. DCNet is composed of three stages: the integration of diversity features, recoding of diversity features, and decoding of integrated features. To enhance the representational capacity and enrich the feature space of a single convolution, we design a diversity convolution block during the first stage. During the second stage, we design a dual-path channel attention mechanism to simultaneously select average and maximum features. In addition, we use a soft Dice loss function to assist in the network’s training. We conducted experiments on the 2017 Automated Cardiac Diagnosis Challenge (ACDC 2017), 2019 Multi-Sequence Cardiac MR Segmentation Challenge (MS-CMRSeg 2019), and 2020 Myocardial Pathology Segmentation Challenge (MyoPS 2020) datasets. We submitted our test results on the ACDC dataset to an online test platform, the proposed DCNet achieved Dice scores of 95.80%, 91.77%, and 91.57% in the left ventricle, right ventricle, and myocardium segmentation tasks, respectively. Compared with four representative networks, the proposed DCNet achieves the best results on balanced steady state free precession (bSSFP) cine sequence and late gadolinium enhancement (LGE) CMR sequences on the MS-CMRSeg and MyoPS datasets. Therefore, the proposed method is promising for automatic ventricle segmentation in clinical applications. We uploaded the code to https://github.com/fly1995/DCNet.

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).

MDFA-Net: Multiscale dual-path feature aggregation network for cardiac segmentation on multi-sequence cardiac MR

Deep convolutional neural networks have shown great potential in medical image segmentation. However, automatic cardiac segmentation is still challenging due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this paper, we propose a multiscale dual-path feature aggregation network (MDFA-Net) to solve misclassification and shape discontinuity problems. The proposed network is aimed to maintain a realistic shape of the segmentation results and divided into two parts: the first part is a non-downsampling multiscale nested network (MN-Net) which restrains the cardiac continuous shape and maintains the shallow information, and the second part is a non-symmetric encoding and decoding network (nSED-Net) that can retain deep details and overcome misclassification. We conducted four-fold cross-validation experiments on balanced steady-state, free precession cine cardiac magnetic resonance (bSSFP cine CMR) sequence, edema-sensitive T2-weighted, black blood spectral presaturation attenuated inversion-recovery (T2-SPAIR) CMR sequence and late gadolinium enhancement (LGE) CMR sequence which include 45 cases in each sequence. The data are provided by the organizer of the Multi-sequence Cardiac MR Segmentation Challenge (MS-CMRSeg 2019) in conjunction with 2019 Medical Image Computing and Computer Assisted Interventions (MICCAI). We also conducted external validation experiments on the data of 2020 MICCAI myocardial pathology segmentation challenge (MyoPS 2020). Whether it is a four-fold cross-validation experiment or an external validation experiment, the proposed method ranks first or second in the segmentation tasks of multi-sequence CMR images. The subjective evaluation also shows the same results as the objective evaluation metrics. The code will be posted at https://github.com/fly1995/MDFA-Net/.

CF Distance: A New Domain Discrepancy Metric and Application to Explicit Domain Adaptation for Cross-Modality Cardiac Image Segmentation

Domain adaptation has great values in unpaired cross-modality image segmentation, where the training images with gold standard segmentation are not available from the target image domain. The aim is to reduce the distribution discrepancy between the source and target domains. Hence, an effective measurement for this discrepancy is critical. In this work, we propose a new metric based on characteristic functions of distributions. This metric, referred to as CF distance, enables explicit domain adaptation, in contrast to the implicit manners minimizing domain discrepancy via adversarial training. Based on this CF distance, we propose an unsupervised domain adaptation framework for cross-modality cardiac segmentation, which consists of image reconstruction and prior distribution matching. We validated the method on two tasks, i.e., the CT-MR cross-modality segmentation and the multi-sequence cardiac MR segmentation. Results showed that the proposed explicit metric was effective in domain adaptation, and the segmentation method delivered promising and superior performance, compared to other state-of-the-art techniques. The data and source code of this work has been released via https://zmiclab.github.io/projects.html.