Deep learning on nonenhanced cardiac MRI data can detect the presence and extent of chronic myocardial infarction. This approach may have potential to reduce use of gadolinium contrast administration. Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non–contrast material–enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating c...

Numerous surveillance data processing is crucial in the Internet-of-Things systems with pervasive edge computing. In this process, salient object detection from surveillance videos plays an important role because it provides the human-concerned semantic cue for various industrial tasks. However, it is still challenging for the existing studies with two aspects. The first one is the redundant saliency information from moving background to disturb the detection of salient objects. The second one is the difficulty to model the spatiotemporal saliency uncertainty. To overcome these challenges. In this article, an intelligent approach is proposed for surveillance saliency detection. It enables a region-proposal-based optical flow strategy to suppress the saliency enhancement of non-salient regions due to the moving background. Besides, it develops the bidirectional Bayesian state transition strategy to model the motion uncertainty for refining the spatiotemporal saliency feature. Extensive experiments have been performed on two datasets (the increase of F _β is larger than 0.01 for DAVIS, and larger than 0.015 for UVSD), and the comparison with seven methods to evaluate the effectiveness of the proposed approach.

Automatic segmentation of myocardial infarction(MI) regions in late gadolinium-enhanced cardiac magnetic resonance images is an essential step in the computed diagnosis of myocardial infarction. Most of the current myocardial infarction region segmentation methods are based on fully supervised deep learning. However, cardiologists' annotation of myocardial infarction regions in cardiac magnetic resonance images during the diagnosis process is time-consuming and expensive. This paper proposes a semi-supervised myocardial infarction segmentation. It consists of two models: 1) a boundary mining model and 2) an adversarial learning model. The boundary mining model can solve the boundary ambiguity problem by enlarging the gap between the foreground and background features, thus segmenting the myocardial infarction region accurately. The adversarial learning model can make the boundary mining model learn from additional unlabeled data by evaluating the segmentation performance and providing pseudo supervision, which significantly increases the robustness of the boundary mining model. We conduct extensive experiments on an in-house myocardial magnetic resonance dataset. The experimental results on six evaluation metrics demonstrate that our method achieves excellent results in myocardial infarction segmentation and outperforms the state-of-the-art semi-supervised methods.

3D semi-supervised medical image segmentation is extremely essential in computer-aided diagnosis, which can reduce the time-consuming task of performing annotation. The challenges with current 3D semi-supervised segmentation algorithms includes the methods, limited attention to volume-wise context information, their inability to generate accurate pseudo labels and a failure to capture important details during data augmentation. This article proposes a dual uncertainty-guided mixing consistency network for accurate 3D semi-supervised segmentation, which can solve the above challenges. The proposed network consists of a Contrastive Training Module which improves the quality of augmented images by retaining the invariance of data augmentation between original data and their augmentations. The Dual Uncertainty Strategy calculates dual uncertainty between two different models to select a more confident area for subsequent segmentation. The Mixing Volume Consistency Module that guides the consistency between mixing before and after segmentation for final segmentation, uses dual uncertainty and can fully learn volume-wise context information. Results from evaluative experiments on brain tumor and left atrial segmentation shows that the proposed method outperforms state-of-the-art 3D semi-supervised methods as confirmed by quantitative and qualitative analysis on datasets. This effectively demonstrates that this study has the potential to become a medical tool for accurate segmentation. Code is available at: https://github.com/yang6277/DUMC .

Deep reinforcement learning (DRL) has demonstrated impressive performance in medical image segmentation, particularly for low-contrast and small medical objects. However, current DRL-based segmentation methods face limitations due to the optimization of error propagation in two separate stages and the need for a significant amount of labeled data. In this paper, we propose a novel deep generative adversarial reinforcement learning (DGARL) approach that, for the first time, enables end-to-end semi-supervised medical image segmentation in the DRL domain. DGARL ingeniously establishes a pipeline that integrates DRL and generative adversarial networks (GANs) to optimize both detection and segmentation tasks holistically while mutually enhancing each other. Specifically, DGARL introduces two innovative components to facilitate this integration in semi-supervised settings. First, a task-joint GAN with two discriminators links the detection results to the GAN’s segmentation performance evaluation, allowing simultaneous joint evaluation and feedback. This ensures that DRL and GAN can be directly optimized based on each other’s results. Second, a bidirectional exploration DRL integrates backward exploration and forward exploration to ensure the DRL agent explores the correct direction when forward exploration is disabled due to lack of explicit rewards. This mitigates the issue of unlabeled data being unable to provide rewards and rendering DRL unexplorable. Comprehensive experiments on three generalization datasets, comprising a total of 640 patients, demonstrate that our novel DGARL achieves 85.02% Dice and improves at least 1.91% for brain tumors, achieves 73.18% Dice and improves at least 4.28% for liver tumors, and achieves 70.85% Dice and improves at least 2.73% for pancreas compared to the ten most recent advanced methods, our results attest to the superiority of DGARL. Code is available at GitHub.