DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation.

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.

TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography.

OBJECTIVE: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches. METHODS AND PROCEDURES: Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches. RESULTS: On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches. CONCLUSION: TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods. CLINICAL IMPACT: This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques.

ColonNet: A novel polyp segmentation framework based on LK-RFB and GPPD.

Colorectal cancer (CRC) holds the distinction of being the most prevalent malignant tumor affecting the digestive system. It is a formidable global health challenge, as it ranks as the fourth leading cause of cancer-related fatalities around the world. Despite considerable advancements in comprehending and addressing colorectal cancer (CRC), the likelihood of recurring tumors and metastasis remains a major cause of high morbidity and mortality rates during treatment. Currently, colonoscopy is the predominant method for CRC screening. Artificial intelligence has emerged as a promising tool in aiding the diagnosis of polyps, which have demonstrated significant potential. Unfortunately, most segmentation methods face challenges in terms of limited accuracy and generalization to different datasets, especially the slow processing and analysis speed has become a major obstacle. In this study, we propose a fast and efficient polyp segmentation framework based on the Large-Kernel Receptive Field Block (LK-RFB) and Global Parallel Partial Decoder(GPPD). Our proposed ColonNet has been extensively tested and proven effective, achieving a DICE coefficient of over 0.910 and an FPS of over 102 on the CVC-300 dataset. In comparison to the state-of-the-art (SOTA) methods, ColonNet outperforms or achieves comparable performance on five publicly available datasets, establishing a new SOTA. Compared to state-of-the-art methods, ColonNet achieves the highest FPS (over 102 FPS) while maintaining excellent segmentation results, achieving the best or comparable performance on the five public datasets. The code will be released at: https://github.com/SPECTRELWF/ColonNet.

Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning.

In this work, we address the task of tumor cellularity (TC) estimation with a novel framework based on the label distribution learning (LDL) paradigm. We propose a self-ensemble label distribution learning framework (SLDL) to resolve the challenges of existing LDL-based methods, including difficulties for inter-rater ambiguity exploitation, proper and flexible label distribution generation, and accurate TC value recovery. The proposed SLDL makes four main contributions which have been demonstrated to be quite effective in numerous experiments. First, we propose an expertness-aware conditional VAE for diversified single-rater modeling and an attention-based multi-rater fusion strategy that enables effective inter-rater ambiguity exploitation. Second, we propose a template-based label distribution generation method that is tailored for the TC estimation task and constructs label distributions based on the annotation priors. Third, we propose a novel restricted distribution loss, significantly improving the TC value estimation by effectively regularizing the learning with unimodal loss and regression loss. Fourth, to the best of our knowledge, we are the first to simultaneously leverage inter-rater and intra-rater variability to address the label ambiguity issue in the breast tumor cellularity estimation tasks. The experimental results on the public BreastPathQ dataset demonstrate that the SLDL outperforms the existing methods by a large margin and achieves new state-of-the-art results in the TC estimation task. The code will be available from https://github.com/PerceptionComputingLab/ULTRA.

Curriculum label distribution learning for imbalanced medical image segmentation.

Label distribution learning (LDL) has the potential to resolve boundary ambiguity in semantic segmentation tasks. However, existing LDL-based segmentation methods suffer from severe label distribution imbalance: the ambiguous label distributions contain a small fraction of the data, while the unambiguous label distributions occupy the majority of the data. The imbalanced label distributions induce model-biased distribution learning and make it challenging to accurately predict ambiguous pixels. In this paper, we propose a curriculum label distribution learning (CLDL) framework to address the above data imbalance problem by performing a novel task-oriented curriculum learning strategy. Firstly, the region label distribution learning (R-LDL) is proposed to construct more balanced label distributions and improves the imbalanced model learning. Secondly, a novel learning curriculum (TCL) is proposed to enable easy-to-hard learning in LDL-based segmentation by decomposing the segmentation task into multiple label distribution estimation tasks. Thirdly, the prior perceiving module (PPM) is proposed to effectively connect easy and hard learning stages based on the priors generated from easier stages. Benefiting from the balanced label distribution construction and prior perception, the proposed CLDL effectively conducts a curriculum learning-based LDL and significantly improves the imbalanced learning. We evaluated the proposed CLDL using the publicly available BRATS2018 and MM-WHS2017 datasets. The experimental results demonstrate that our method significantly improves different segmentation metrics compared to many state-of-the-art methods. The code will be available.1.

Trajectory-Aware Adaptive Imaging Clue Analysis for Guidewire Artifact Removal in Intravascular Optical Coherence Tomography.

Guidewire Artifact Removal (GAR) involves restoring missing imaging signals in areas of IntraVascular Optical Coherence Tomography (IVOCT) videos affected by guidewire artifacts. GAR helps overcome imaging defects and minimizes the impact of missing signals on the diagnosis of CardioVascular Diseases (CVDs). To restore the actual vascular and lesion information within the artifact area, we propose a reliable Trajectory-aware Adaptive imaging Clue analysis Network (TAC-Net) that includes two innovative designs: (i) Adaptive clue aggregation, which considers both texture-focused original (ORI) videos and structure-focused relative total variation (RTV) videos, and suppresses texture-structure imbalance with an active weight-adaptation mechanism; (ii) Trajectory-aware Transformer, which uses a novel attention calculation to perceive the attention distribution of artifact trajectories and avoid the interference of irregular and non-uniform artifacts. We provide a detailed formulation for the procedure and evaluation of the GAR task and conduct comprehensive quantitative and qualitative experiments. The experimental results demonstrate that TAC-Net reliably restores the texture and structure of guidewire artifact areas as expected by experienced physicians (e.g., SSIM: 97.23%). We also discuss the value and potential of the GAR task for clinical applications and computer-aided diagnosis of CVDs.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network.

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.

SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions.

Motivation: Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information. Results: In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life. Availability and implementation: The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.

A contrastive consistency semi-supervised left atrium segmentation model.

Accurate segmentation for the left atrium (LA) is a key process of clinical diagnosis and therapy for atrial fibrillation. In clinical, the semantic-level segmentation of LA consumes much time and labor. Although supervised deep learning methods can somewhat solve this problem, a high-efficient deep learning model requires abundant labeled data that is hard to acquire. Therefore, the research on automatic LA segmentation of leveraging unlabeled data is highly required. In this paper, we propose a semi-supervised LA segmentation framework including a segmentation model and a classification model. The segmentation model takes volumes from both labeled and unlabeled data as input and generates predictions of LAs. And then, a classification model maps these predictions to class-vectors for each input. Afterward, to leverage the class information, we construct a contrastive consistency loss function based on these class-vectors, so that the model can enlarge the discrepancy of the inter-class and compact the similarity of the intra-class for learning more distinguishable representation. Moreover, we set the class-vectors from the labeled data as references to the class-vectors from the unlabeled data to relieve the influence of the unreliable prediction for the unlabeled data. At last, we evaluate our semi-supervised LA segmentation framework on a public LA dataset using four universal metrics and compare it with recent state-of-the-art models. The proposed model achieves the best performance on all metrics with a Dice Score of 89.81 %, Jaccard of 81.64 %, 95 % Hausdorff distance of 7.15 mm, and Average Surface Distance of 1.82 mm. The outstanding performance of the proposed framework shows that it may have a significant contribution to assisting the therapy of patients with atrial fibrillation. Code is available at: https://github.com/PerceptionComputingLab/SCC.

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/).

KIEGLFN: A unified acne grading framework on face images.

BACKGROUND AND OBJECTIVE: Grading the severity level is an extremely important procedure for correct diagnoses and personalized treatment schemes for acne. However, the acne grading criteria are not unified in the medical field. This work aims to develop an acne diagnosis system that can be generalized to various criteria. METHODS: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed. It imitates the global estimation of the dermatologist diagnosis in two steps. First, an adaptive image preprocessing method effectively filters meaningless information and enhances key information. Next, an innovative network structure fuses global deep features with local features to simulate the dermatologists' comparison of local skin and global observation. In addition, a transfer fine-tuning strategy is proposed to transfer prior knowledge on one criterion to another criterion, which effectively improves the framework performance in case of insufficient data. RESULTS: The Preprocessing method effectively filters meaningless areas and improves the performance of downstream models.The framework reaches accuracies of 84.52% and 59.35% on two datasets separately. CONCLUSIONS: The application of the framework on acne grading exceeds the state-of-the-art method by 1.71%, reaches the diagnostic level of a professional dermatologist and the transfer fine-tuning strategy improves the accuracy of 6.5% on the small data.

Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

OBJECTIVE: To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS). METHODS: A total of 769 breast tumors were enrolled in this study and were randomly divided into training set and test set at 600 vs. 169. The novel DLNs (Resent v2, ResNet50 v2, ResNet101 v2) added a new ASN to the traditional ResNet networks and extracted morphological information of breast tumors. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic (ROC) curve (AUC), and average precision (AP) were calculated. The diagnostic performances of novel DLNs were compared with those of two radiologists with different experience. RESULTS: The ResNet34 v2 model had higher specificity (76.81%) and PPV (82.22%) than the other two, the ResNet50 v2 model had higher accuracy (78.11%) and NPV (72.86%), and the ResNet101 v2 model had higher sensitivity (85.00%). According to the AUCs and APs, the novel ResNet101 v2 model produced the best result (AUC 0.85 and AP 0.90) compared with the remaining five DLNs. Compared with the novice radiologist, the novel DLNs performed better. The F1 score was increased from 0.77 to 0.78, 0.81, and 0.82 by three novel DLNs. However, their diagnostic performance was worse than that of the experienced radiologist. CONCLUSIONS: The novel DLNs performed better than traditional DLNs and may be helpful for novice radiologists to improve their diagnostic performance of breast cancer in ABUS. KEY POINTS: • A novel automatic segmentation network to extract morphological information was successfully developed and implemented with ResNet deep learning networks. • The novel deep learning networks in our research performed better than the traditional deep learning networks in the diagnosis of breast cancer using ABUS images. • The novel deep learning networks in our research may be useful for novice radiologists to improve diagnostic performance.

Uncertainty-guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium-enhanced MRI.

PURPOSE: Atrial fibrillation (AF) is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient noninvasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with AF. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation a challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentation results. METHODS: In this study, we proposed an uncertainty-guided symmetric multilevel supervision (SML) network for 3D LA segmentation in LGE MRI. First, we constructed an SML structure to combine the corresponding features from the encoding and decoding stages to learn the multiscale representation of LA. Second, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on the surface. RESULTS: We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance (HD) of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best HD that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparable performance. CONCLUSIONS: We proposed an efficient automatic LA segmentation model that consisted of an SML structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multilevel structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on the surface.

Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation.

Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1 The code will be available from https://github.com/JohnleeHIT/SLEX-Net. results, none of them incorporated ICH's prior information in their methods. In this work, for the first time, we proposed a novel SLice EXpansion Network (SLEX-Net), which incorporated hematoma expansion in the segmentation architecture by directly modeling the hematoma variation among adjacent slices. Firstly, a new module named Slice Expansion Module (SEM) was built, which can effectively transfer contextual information between two adjacent slices by mapping predictions from one slice to another. Secondly, to perceive contextual information from both upper and lower slices, we designed two information transmission paths: forward and backward slice expansion, and aggregated results from those paths with a novel weighing strategy. By further exploiting intra-slice and inter-slice context with the information paths, the network significantly improved the accuracy and continuity of segmentation results. Moreover, the proposed SLEX-Net enables us to conduct an uncertainty estimation with one-time inference, which is much more efficient than existing methods. We evaluated the proposed SLEX-Net and compared it with some state-of-the-art methods. Experimental results demonstrate that our method makes significant improvements in all metrics on segmentation performance and outperforms other existing uncertainty estimation methods in terms of several metrics.

Dynamically constructed network with error correction for accurate ventricle volume estimation.

Automated ventricle volume estimation (AVVE) on cardiac magnetic resonance (CMR) images is very important for clinical cardiac disease diagnosis. However, current AVVE methods ignore the error correction for the estimated volume. This results in clinically intolerable ventricle volume estimation error and further leads to wrong ejection fraction (EF) assessment, which significantly limits the application potential of AVVE methods. The objective of this paper is to address this problem with AVVE and further make it more clinically applicable. We proposed a dynamically constructed network to achieve accurate AVVE. First, we introduced a novel dynamically constructed deep learning framework, that evolves a single model into a bi-model volume estimation network. In this way, the EF correlation can be built directly based on the bi-model network. Second, we proposed an error correction strategy using dynamically created residual nodes, which is based on stochastic configurations with an EF correlation constraint. Finally, we formulated the proposed method into an end-to-end joint optimization framework for accurate ventricle volume estimation with effective error correction. Experiments and comparisons on large-scale cardiac magnetic resonance datasets were carried out. Results show that the proposed method outperforms state-of-the-art methods, and has good potential for clinical application. Besides, the proposed method is the first work to achieve error correction for AVVE and also has the potential to be extended to other medical index estimation tasks.

Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography.

We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography's challenges: high dimensional data, complex anatomical environments, and limited annotation data. First, we proposed a deep atlas network, which integrated LV atlas into the deep learning framework to address the 3D LV segmentation problem on echocardiography for the first time, and improved the performance based on limited annotation data. Second, we proposed a novel information consistency constraint to enhance the model's performance from different levels simultaneously, and finally achieved effective optimization for 3D LV segmentation on complex anatomical environments. Finally, the proposed method was optimized in an end-to-end back propagation manner and it achieved high inference efficiency even with high dimensional data, which satisfies the efficiency requirement of clinical practice. The experiments proved that the proposed method achieved better segmentation results and a higher inference speed compared with state-of-the-art methods. The mean surface distance, mean hausdorff surface distance, and mean dice index were 1.52 mm, 5.6 mm and 0.97 respectively. What's more, the method is efficient and its inference time is 0.02s. The experimental results proved that the proposed method has a potential clinical application for 3D LV segmentation on echocardiography.

The Role of CaMKII Overexpression and Oxidation in Atrial Fibrillation-A Simulation Study.

This simulation study aims to investigate how the Calcium/calmodulin-dependent protein kinase II (CaMKII) overexpression and oxidation would influence the cardiac electrophysiological behavior and its arrhythmogenic mechanism in atria. A new-built CaMKII oxidation module and a refitted CaMKII overexpression module were integrated into a mouse atrial cell model for analyzing cardiac electrophysiological variations in action potential (AP) characteristics and intracellular Ca2+ cycling under different conditions. Simulation results showed that CaMKII overexpression significantly increased the phosphorylation level of its downstream target proteins, resulting in prolonged AP and smaller calcium transient amplitude, and impaired the Ca2+ cycling stability. These effects were exacerbated by extra reactive oxygen species, which oxidized CaMKII and led to continuous high CaMKII activation in both systolic and diastolic phases. Intracellular Ca2+ depletion and sustained delayed afterdepolarizations (DADs) were observed under co-existing CaMKII overexpression and oxidation, which could be effectively reversed by clamping the phosphorylation level of ryanodine receptor (RyR). We also found that the stability of RyR release highly depended on a delicate balance between the level of RyR phosphorylation and sarcoplasmic reticulum Ca2+ concentration, which was closely related to the genesis of DADs. We concluded that the CaMKII overexpression and oxidation have a synergistic role in increasing the activity of CaMKII, and the unstable RyR may be the key downstream target in the CaMKII arrhythmogenic mechanism. Our simulation provides detailed mechanistic insights into the arrhythmogenic effect of CaMKII overexpression and oxidation, which suggests CaMKII as a promising target in the therapy of atrial fibrillation.

Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification.

Accurate and automated cardiac bi-ventricle quantification based on cardiac magnetic resonance (CMR) image is a very crucial procedure for clinical cardiac disease diagnosis. Two traditional and commensal tasks, i.e., bi-ventricle segmentation and direct ventricle function index estimation, are always independently devoting to address ventricle quantification problem. However, because of inherent difficulties from the variable CMR imaging conditions, these two tasks are still open challenging. In this paper, we proposed a unified bi-ventricle quantification framework based on commensal correlation between the bi-ventricle segmentation and direct area estimation. Firstly, we proposed the area commensal correlation between the two traditional cardiac quantification tasks for the first time, and designed a novel deep commensal network (DCN) to join these two commensal tasks into a unified framework based on the proposed commensal correlation loss. Secondly, we proposed an differentiable area operator to model the proposed area commensal correlation and made the proposed model continuously differentiable. Thirdly, we proposed a high-efficiency and novel uncertainty estimation method through one-time inference based on cross-task output variability. And finally DCN achieved end-to-end optimization and fast convergence as well as uncertainty estimation with one-time inference. Experiments on the four open accessible short-axis CMR benchmark datasets (i.e., Sunnybrook, STACOM 2011, RVSC, and ACDC) showed that the proposed method achieves best bi-ventricle quantification accuracy and optimization performance. Hence, the proposed method has big potential to be extended to other medical image analysis tasks and has clinical application value.

A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography.

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.