Joint Metric Learning-Based Class-Specific Representation for Image Set Classification

With the rapid advances in digital imaging and communication technologies, recently image set classification has attracted significant attention and has been widely used in many real-world scenarios. As an effective technology, the class-specific representation theory-based methods have demonstrated their superior performances. However, this type of methods either only uses one gallery set to measure the gallery-to-probe set distance or ignores the inner connection between different metrics, leading to the learned distance metric lacking robustness, and is sensitive to the size of image sets. In this article, we propose a novel joint metric learning-based class-specific representation framework (JMLC), which can jointly learn the related and unrelated metrics. By iteratively modeling probe set and related or unrelated gallery sets as affine hull, we reconstruct this hull sparsely or collaboratively over another image set. With the obtained representation coefficients, the combined metric between the query set and the gallery set can then be calculated. In addition, we also derive the kernel extension of JMLC and propose two new unrelated set constituting strategies. Specifically, kernelized JMLC (KJMLC) embeds the gallery sets and probe sets into the high-dimensional Hilbert space, and in the kernel space, the data become approximately linear separable. Extensive experiments on seven benchmark databases show the superiority of the proposed methods to the state-of-the-art image set classifiers.

[Low-dose CT reconstruction based on high-dimensional partial differential equation projection recovery]

OBJECTIVE

We propose a low-dose CT reconstruction method using partial differential equation (PDE) denoising under high-dimensional constraints.

METHODS

The projection data were mapped into a high-dimensional space to construct a high-dimensional representation of the data, which were updated by moving the points in the high-dimensional space. The data were denoised using partial differential equations and the CT image was reconstructed using the FBP algorithm.

RESULTS

Compared with those by FBP, PWLS-QM and TGV-WLS methods, the relative root mean square error of the Shepp-Logan image reconstructed by the proposed method were reduced by 68.87%, 50.15% and 27.36%, the structural similarity values were increased by 23.50%, 8.83% and 1.62%, and the feature similarity values were increased by 17.30%, 2.71% and 2.82%, respectively. For clinical image reconstruction, the proposed method, as compared with FBP, PWLS-QM and TGV-WLS methods, resulted in reduction of the relative root mean square error by 42.09%, 31.04% and 21.93%, increased the structural similarity values by 18.33%, 13.45% and 4.63%, and increased the feature similarity values by 3.13%, 1.46% and 1.10%, respectively.

CONCLUSION

The new method can effectively reduce the streak artifacts and noises while maintaining the spatial resolution in reconstructed low-dose CT images.

Exploiting Sparse Self-Representation and Particle Swarm Optimization for CNN Compression

Structured pruning has received ever-increasing attention as a method for compressing convolutional neural networks. However, most existing methods directly prune the network structure according to the statistical information of the parameters. Besides, these methods differentiate the pruning rates only in each pruning stage or even use the same pruning rate across all layers, rather than using learnable parameters. In this article, we propose a network redundancy elimination approach guided by the pruned model. Our proposed method can easily tackle multiple architectures and is scalable to the deeper neural networks because of the use of joint optimization during the pruning procedure. More specifically, we first construct a sparse self-representation for the filters or neurons of the well-trained model, which is useful for analyzing the relationship among filters. Then, we employ particle swarm optimization to learn pruning rates in a layerwise manner according to the performance of the pruned model, which can determine optimal pruning rates with the best performance of the pruned model. Under this criterion, the proposed pruning approach can remove more parameters without undermining the performance of the model. Experimental results demonstrate the effectiveness of our proposed method on different datasets and different architectures. For example, it can reduce 58.1% FLOPs for ResNet50 on ImageNet with only a 1.6% top-five error increase and 44.1% FLOPs for FCN_ResNet50 on COCO2017 with a 3% error increase, outperforming most state-of-the-art methods.

Self-supervised learning with application for infant cerebellum segmentation and analysis

Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

Associations of Microvascular Dysfunction with Mild Cognitive Impairment and Cognitive Function Among Rural-Dwelling Older Adults in China

BACKGROUND

Microvascular dysfunction (MVD) may contribute to cognitive impairment and Alzheimer's disease, but evidence is limited.

OBJECTIVE

To investigate the association of composite and organ-specific MVD burden with mild cognitive impairment (MCI) and cognition among rural-dwelling Chinese older adults.

METHODS

In this population-based cross-sectional study, we assessed MVD makers using optical coherence tomographic angiography for retinal microvasculature features, brain magnetic resonance imaging scans for cerebral small vessel disease (CSVD), and serum biomarkers for MVD. A composite MVD score was generated from the aforementioned organ-specific parameters. We used a neuropsychological test battery to assess memory, verbal fluency, attention, executive function, and global cognitive function. MCI, amnestic MCI (aMCI), and non-amnestic MCI (naMCI) were diagnosed following the Petersen's criteria. Data was analyzed with the linear and logistic regression models.

RESULTS

Of the 274 dementia-free participants (age≥65 years), 56 were diagnosed with MCI, including 47 with aMCI and 9 with naMCI. A composite MVD score was statistically significantly associated with an odds ratio (OR) of 2.70 (95% confidence interval 1.12-6.53) for MCI and β-coefficient of -0.29 (-0.48--0.10) for global cognitive score after adjustment for socio-demographics, lifestyle factors, APOE genotype, the Geriatric Depression Scale score, serum inflammatory biomarkers, and cardiovascular comorbidity. A composite score of retinal microvascular morphology was associated with a multivariable-adjusted OR of 1.72 (1.09-2.73) for MCI and multivariable-adjusted β-coefficient of -0.11 (-0.22--0.01) for global cognitive score. A composite CSVD score was associated with a lower global cognitive score (β= -0.10; -0.17--0.02).

CONCLUSION

Microvascular dysfunction, especially in the brain and retina, is associated with MCI and poor cognitive function among rural-dwelling older adults.

A novel time series prediction method based on pooling compressed sensing echo state network and its application in stock market

In the prediction of time series, the echo state network (ESN) exhibits exclusive strengths and a unique training structure. Based on ESN model, a pooling activation algorithm consisting noise value and adjusted pooling algorithm is proposed to enrich the update strategy of the reservoir layer in ESN. The algorithm optimizes the distribution of reservoir layer nodes. And the nodes set will be more matched to the characteristics of the data. In addition, we introduce a more efficient and accurate compressed sensing technique based on the existing research. The novel compressed sensing technique reduces the amount of spatial computation of methods. The ESN model based on the above two techniques overcomes the limitations in traditional prediction. In the experimental part, the model is validated with different chaotic time series as well as multiple stocks, and the method shows its efficiency and accuracy in prediction.

Effect of Avian Influenza Virus subtype H9N2 on the expression of complement-associated genes in chicken erythrocytes

The H9N2 subtype avian influenza virus can infect both chickens and humans. Previous studies have reported a role for erythrocytes in immunity. However, the role of H9N2 against chicken erythrocytes and the presence of complement-related genes in erythrocytes has not been studied. This research investigated the effect of H9N2 on complement-associated gene expression in chicken erythrocytes. The expression of complement-associated genes (C1s, C1q, C2, C3, C3ar1, C4, C4a, C5, C5ar1, C7, CD93 and CFD) was detected by reverse transcription-polymerase chain reaction (RT-PCR). Quantitative Real-Time PCR (qRT-PCR) was used to analyse the differential expression of complement-associated genes in chicken erythrocytes at 0 h, 2 h, 6 h and 10 h after the interaction between H9N2 virus and chicken erythrocytes in vitro and 3, 7 and 14 d after H9N2 virus nasal infection of chicks. Expression levels of C1q, C4, C1s, C2, C3, C5, C7 and CD93 were significantly up-regulated at 2 h and significantly down-regulated at 10 h. Gene expression levels of C1q, C3ar1, C4a, CFD and C5ar1 were seen to be different at each time point. The expression levels of C1q, C4, C1s, C2, C3, C5, C7, CFD, C3ar1, C4a and C5ar1 were significantly up-regulated at 7 d and the gene expression of levels of C3, CD93 and C5ar1 were seen to be different at each time point. The results confirmed that all the complement-associated genes were expressed in chicken erythrocytes and showed the H9N2 virus interaction with chicken erythrocytes and subsequent regulation of chicken erythrocyte complement-associated genes expression. This study reported, for the first time, the relationship between H9N2 and complement system of chicken erythrocytes, which will provide a foundation for further research into the prevention and control of H9N2 infection.

Associations of macular microvascular parameters with cerebral small vessel disease in rural older adults: A population-based OCT angiography study

Objective

To explore the associations of macular microvascular parameters with cerebral small vessel disease (CSVD) in rural-dwelling older adults in China.

Methods

This population-based cross-sectional study included 195 participants (age ≥ 60 years; 57.4% women) in the optical coherence tomographic angiography (OCTA) sub-study within the Multimodal Interventions to delay Dementia and disability in rural China (MIND-China). Macular microvascular parameters were measured using the OCTA. We automatically estimated volumes of gray matter, white matter, and white matter hyperintensity (WMH), and manually assessed numbers of enlarged perivascular spaces (EPVS) and lacunes on brain magnetic resonance imaging. Data were analyzed with the general linear models.

Results

Adjusting for multiple confounders, lower vessel skeleton density (VSD) and higher vessel diameter index (VDI) were significantly associated with larger WMH volume (P < 0.05). Lower VSD and foveal density-300 (FD-300) of left eye were significantly associated with lower brain parenchymal volume (P < 0.05). In addition, lower areas of foveal avascular zone (FAZ) and FD-300 of left eye were significantly associated with more EPVS (P < 0.05). The associations of abnormal macular microvascular parameters with WMH volume were evident mainly among females. Macular microvascular parameters were not associated with lacunes.

Conclusion

Macular microvascular signs are associated with WMH, brain parenchymal volume, and EPVS in older adults. The OCTA-assessed macular microvascular parameters can be valuable markers for microvascular lesions in the brain.

Self-training adversarial learning for cross-domain retinal OCT fluid segmentation

Accurate measurements of the size, shape and volume of macular edema can provide important biomarkers to jointly assess disease progression and treatment outcome. Although many deep learning-based segmentation algorithms have achieved remarkable success in semantic segmentation, these methods have difficulty obtaining satisfactory segmentation results in retinal optical coherence tomography (OCT) fluid segmentation tasks due to low contrast, blurred boundaries, and varied distribution. Moreover, directly applying a well-trained model on one device to test the images from other devices may cause the performance degradation in the joint analysis of multi-domain OCT images. In this paper, we propose a self-training adversarial learning framework for unsupervised domain adaptation in retinal OCT fluid segmentation tasks. Specifically, we develop an image style transfer module and a fine-grained feature transfer module to reduce discrepancies in the appearance and high-level features of images from different devices. Importantly, we transfer the target images to the appearance of source images to ensure that no image information of the source domain for supervised training is lost. To capture specific features of the target domain, we design a self-training module based on a discrepancy and similarity strategy to select the images with better segmentation results from the target domain and then introduce them into the source domain for the iterative training segmentation model. Extensive experiments on two challenging datasets demonstrate the effectiveness of our proposed method. In Particular, our proposed method achieves comparable results on cross-domain retinal OCT fluid segmentation compared with the state-of-the-art methods.

Invasive fungal infection is associated with antibiotic exposure in preterm infants: a multi-centre prospective case-control study

BACKGROUND

Previous antibiotic exposure is an important risk factor for invasive fungal infection (IFI). Antibiotic overexposure is common in lower-income countries; however, multi-centre studies concerning IFI in relation to antibiotic exposure are scarce.

AIM

This prospective, multi-centre matched case-control study explored the correlation of IFI and antibiotic exposure in very preterm infants or very-low-birthweight infants admitted to 23 tertiary hospitals in China between 2018 and 2021.

METHODS

Using a 1:2 matched design for gestational age, birth weight and early-onset sepsis (yes/no), the risk factors between infants diagnosed with IFI and infection-free controls were compared. The antibiotic use rate (AUR) was calculated using calendar days of antibiotic therapy in the 4 weeks preceding IFI onset divided by onset day of IFI.

FINDINGS

In total, 6368 infants were included in the study, of which 90 (1.4%) were diagnosed with IFI. Median AUR, length of antibiotic therapy (LOT) and days of antibiotic therapy (DOT) within the 4 weeks preceding IFI onset were 0.90, 18 days and 30 days, respectively. Multi-variate analysis showed that a 10% increase in AUR, each additional day of DOT and LOT, and each additional day of third-generation cephalosporins and carbapenems were notably associated with IFI.

CONCLUSION

Prolonged antibiotic therapy is common before the onset of IFI, and is an important risk factor, especially the use of third-generation cephalosporins and carbapenems. Antibiotic stewardship should be urgently developed and promoted for preterm infants in order to reduce IFI in lower-income countries such as China.

Fixed/Predefined-time synchronization of memristor-based complex-valued BAM neural networks for image protection

This paper investigates the fixed-time synchronization and the predefined-time synchronization of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs) with leakage time-varying delay. First, the proposed neural networks are regarded as two dynamic real-valued systems. By designing a suitable feedback controller, combined with the Lyapunov method and inequality technology, a more accurate upper bound of stability time estimation is given. Then, a predefined-time stability theorem is proposed, which can easily establish a direct relationship between tuning gain and system stability time. Any predefined time can be set as controller parameters to ensure that the synchronization error converges within the predefined time. Finally, the developed chaotic MCVBAMNNs and predefined-time synchronization technology are applied to image encryption and decryption. The correctness of the theory and the security of the cryptographic system are verified by numerical simulation.

A new predefined-time stability theorem and its application in the synchronization of memristive complex-valued BAM neural networks

In this paper, two novel and general predefined-time stability lemmas are given and applied to the predefined-time synchronization problem of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs). Firstly, different from the generally fixed-time stability lemma, the setting of an adjustable time parameter in the derived predefined-time stability lemma causes it to be more flexible and more general. Secondly, the model studied in the complex-valued BAM neural networks model, which is different from the previous discussion of the real part and imaginary part respectively. It is more practical to study the complex-valued nonseparation. Thirdly, two effective controllers are designed to realize the synchronization performance of BAM neural networks based on the predefined-time stability, and the analysis is given based on general predefined-time synchronization. Finally, the correctness of the theoretical derivation is verified by numerical simulation. A secure communication scheme based on predefined-time synchronization of MCVBAMNNs is proposed, and the effectiveness and superiority of the results are proved.

Effect of Fenton pretreatment and bacterial inoculation on cellulose-degrading genes and fungal communities during rice straw composting

The aims of this article were to study the effect of Fenton pretreatment and bacterial inoculation on cellulose-degrading genes and fungal communities during rice straw composting. The rice straw was pretreated by Fenton reactions and functional bacterial agents were then inoculated during the cooling phase of composting. Three treatment groups were carried out, the control (CK), Fenton pretreatment (FeW) and Fenton pretreatment and bacterial inoculation (FeWI). The results indicated that Fenton pretreatment and bacterial inoculation changed the fungal communities composition and increased fungal diversity, leading to changes in the cellulose-degrading genes. In addition, a network analysis showed that in the FeWI treatment, the fungi from modules 1, 5 and 8 were core hosts of the cellulose-degrading genes driving the cellulosic degradation. Moreover, Fenton pretreatment and bacterial inoculation changed the core module fungal communities and strengthened the correlation between the core fungi and the cellulose-degrading genes, thereby promoting cellulosic degradation. Based on redundancy and structural equation model analyses, the NH₄⁺-N, TOC, pH and Shannon index were important factors influencing the variations in the cellulose-degrading genes. This study provides a foundation for cellulosic degradation during cellulosic waste composting.

Predefined-Time Stability/Synchronization of Coupled Memristive Neural Networks With Multi-Links and Application in Secure Communication

This paper explores the realization of a predefined-time synchronization problem for coupled memristive neural networks with multi-links (MCMNN) via nonlinear control. Several effective conditions are obtained to achieve the predefined-time synchronization of MCMNN based on the controller and Lyapunov function. Moreover, the settling time can be tunable based on a parameter designed by the controller, which is more flexible than fixed-time synchronization. Then based on the predefined-time stability criterion and the tunable settling time, we propose a secure communication scheme. This scheme can determine security of communication in the aspect of encrypting the plaintext signal with the participation of multi-links topology and coupled form. Meanwhile, the plaintext signals can be recovered well according to the given new predefined-time stability theorem. Finally, numerical simulations are given to verify the effectiveness of the obtained theoretical results and the feasibility of the secure communication scheme.

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2 years of age based on abnormal behaviors. Existing neuroimaging‐based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI‐based studies include subjects older than 5 years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24 months of age. Specifically, by leveraging an infant‐dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state‐of‐the‐art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject‐specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early‐stage status prediction of ASD by sMRI.

Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts

Accurate tissue segmentation of large-scale pediatric brain MR images from multiple sites is essential to characterize early brain development. Due to imaging motion/Gibbs artifacts and multi-site issue (or domain shift issue), it remains a challenge to accurately segment brain tissues from multi-site pediatric MR images. In this paper, we present a multi-scale self-supervised learning (M-SSL) framework to accurately segment tissues for multi-site pediatric brain MR images with artifacts. Specifically, we first work on the downsampled images to estimate coarse tissue probabilities and build a global anatomic guidance. We then train another segmentation model based on the original images to estimate fine tissue probabilities, which are further integrated with the global anatomic guidance to refine the segmentation results. In the testing stage, to alleviate the multi-site issue, we propose an iterative self-supervised learning strategy to train a site-specific segmentation model based on a set of reliable training samples automatically generated for a to-be-segmented site. The experimental results on pediatric brain MR images with real artifacts and multi-site subjects from the iSeg2019 challenge demonstrate that our M-SSL method achieves better performance compared with several state-of-the-art methods.

[Treatment strategies and research progress of acute ilio-femoral deep vein thrombosis]

In the past,treatment of acute ilio-femoral deep vein thrombosis (IFDVT) was mainly based on anticoagulation alone,but 30%-50% of patients will develop post-thrombotic syndrome,causing a serious medical burden.Thrombus removal technology such as catheter-directed thrombolysis and percutaneous mechanical thrombectomy can effectively remove blood clots and compensate for the deficiencies of simple anticoagulation,which is expected to improve the prognosis of such disease,but the current evidence is insufficient,and other treatments such as filter implantation and compression therapy are also controversial.This article summarizes the treatment strategies and the latest progress of acute IFDVT,hoping to help the treatment of this type of disease.

DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network

Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning

Automated lesion segmentation is one of the important tasks for the quantitative assessment of retinal diseases in SD-OCT images. Recently, deep convolutional neural networks (CNN) have shown promising advancements in the field of automated image segmentation, whereas they always benefit from large-scale datasets with high-quality pixel-wise annotations. Unfortunately, obtaining accurate annotations is expensive in both human effort and finance. In this paper, we propose a weakly supervised two-stage learning architecture to detect and further segment central serous chorioretinopathy (CSC) retinal detachment with only image-level annotations. Specifically, in the first stage, a Located-CNN is designed to detect the location of lesion regions in the whole SD-OCT retinal images, and highlight the distinguishing regions. To generate available a pseudo pixel-level label, the conventional level set method is employed to refine the distinguishing regions. In the second stage, we customize the active-contour loss function in deep networks to achieve the effective segmentation of the lesion area. A challenging dataset is used to evaluate our proposed method, and the results demonstrate that the proposed method consistently outperforms some current models trained with a different level of supervision, and is even as competitive as those relying on stronger supervision. To our best knowledge, we are the first to achieve CSC segmentation in SD-OCT images using weakly supervised learning, which can greatly reduce the labeling efforts.

An integrated time adaptive geographic atrophy prediction model for SD-OCT images

The automated prediction of geographic atrophy (GA) lesion growth can help ophthalmologists understand how the GA progresses, and assess the efficiency of current treatment and the prognosis of the disease. We developed an integrated time adaptive prediction model for identifying the location of future GA growth. The proposed model was comprised of bi-directional long short-term memory (BiLSTM) network-based prediction module and convolutional neural network (CNN)-based refinement module. Considering the discontinuity of time intervals among sequential follow-up visits, we integrated time factors into BiLSTM-based prediction module to control the time attribute expediently. Then, the results from prediction module were refined by a CNN-based strategy to obtain the final locations of future GA growth. The 10 scenarios were designed to evaluate the prediction accuracy of our proposed model. The 1-6th scenarios demonstrated the importance of the prior information similarity, the 7-8th scenarios verified the effect of time factors and refinement methods respectively and the 9th scenario compared the prediction results between those using a single follow-up visit for training and using 2 sequential follow-up visits for training. The 10th scenario showed the model generalization performance across regions. The average dice indexes (DI) of the predicted GA regions in the 1-6th scenarios are 0.86, 0.89, 0.89, 0.92 and 0.88, 0.90, respectively. By integrating time factors to the BiLSTM models, the prediction accuracy was improved by almost 10%. The CNN-based refinement strategy can remove the wrong GA regions effectively to preserve the actual GA regions and improve the prediction accuracy further. The prediction results based on 2 sequential follow-up visits showed higher correlations than that based on single follow-up visit. The proposed model presented a good generalization performance while training patients and testing patients were from different regions. Experimental results demonstrated the importance of prior information to the prediction accuracy. We demonstrate the feasibility of creating a model for disease prediction.

Glutathione-S-transferase A3 protein suppresses thiram-induced tibial dyschondroplasia by regulating prostaglandin-related genes expression

Tibial dyschondroplasia (TD) is an intractable avian cartilage disease in which proximal growth plates of tibia lack blood vessels and contain nonviable cells, and it leads to the inflammatory response. Prostaglandins (PGs) genes have not been studied yet in TD chicken, and they might play role in skeletal metabolism, therefore we planned to explore the role of recombinant glutathione-S-transferase A3 (rGSTA3) protein and PG-related genes. In this study, qRT-PCR, enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) analysis were used to identify the expression patterns of eight PG-related genes in the tibial growth plate of broiler chicken. The results showed that the expression of PG-related genes glutathione-S-transferase A3 (GSTA3), cyclooxygenase 2 (COX-2), prostaglandin D2 synthase (PTGDS), prostaglandin E synthase (PTGES), prostaglandin E2 receptor (PTGER) 3, PTGER4, prostaglandin reductase 1 (PTGR1) and hematopoietic prostaglandin D synthases (HPGDS) expression were identified and could significantly respond to thiram-induced TD chicken. Interestingly, the expression of rate-limiting enzyme COX-2 and PGE₂ were induced after the treatment of rGSTA3 protein. These findings demonstrated that the occurrence of TD is closely related to the inhibition of PGs. Moreover, rGSTA3 protein participated in the recovery of TD by strengthening the expression of PG-related genes.

Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation

To characterize early cerebellum development, accurate segmentation of the cerebellum into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) tissues is one of the most pivotal steps. However, due to the weak tissue contrast, extremely folded tiny structures, and severe partial volume effect, infant cerebellum tissue segmentation is especially challenging, and the manual labels are hard to obtain and correct for learning-based methods. To the best of our knowledge, there is no work on the cerebellum segmentation for infant subjects less than 24 months of age. In this work, we develop a semi-supervised transfer learning framework guided by a confidence map for tissue segmentation of cerebellum MR images from 24-month-old to 6-month-old infants. Note that only 24-month-old subjects have reliable manual labels for training, due to their high tissue contrast. Through the proposed semi-supervised transfer learning, the labels from 24-month-old subjects are gradually propagated to the 18-, 12-, and 6-month-old subjects, which have a low tissue contrast. Comparison with the state-of-the-art methods demonstrates the superior performance of the proposed method, especially for 6-month-old subjects.

Informative Feature-Guided Siamese Network for Early Diagnosis of Autism

Autism, or autism spectrum disorder (ASD), is a complex developmental disability, and usually diagnosed with observations at around 3-4 years old based on behaviors. Studies have indicated that the early treatment, especially during early brain development in the first two years of life, can significantly improve the symptoms, therefore, it is important to identify ASD as early as possible. Most previous works employed imaging-based biomarkers for the early diagnosis of ASD. However, they only focused on extracting features from the intensity images, ignoring the more informative guidance from segmentation and parcellation maps. Moreover, since the number of autistic subjects is always much smaller than that of normal subjects, this class-imbalance issue makes the ASD diagnosis more challenging. In this work, we propose an end-to-end informative feature-guided Siamese network for the early ASD diagnosis. Specifically, besides T1w and T2w images, the discriminative features from segmentation and parcellation maps are also employed to train the model. To alleviate the class-imbalance issue, the Siamese network is utilized to effectively learn what makes the pair of inputs belong to the same class or different classes. Furthermore, the subject-specific attention module is incorporated to identify the ASD-related regions in an end-to-end fully automatic learning manner. Both ablation study and comparisons demonstrate the effectiveness of the proposed method, achieving an overall accuracy of 85.4%, sensitivity of 80.8%, and specificity of 86.7%.

MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images

As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions affected by GA is a fundamental step for clinical diagnosis. In this paper, we present a novel weakly supervised model for GA segmentation in Spectral-Domain Optical Coherence Tomography (SD-OCT) images. A novel Multi-Scale Class Activation Map (MS-CAM) is proposed to highlight the discriminatory significance regions in localization and detail descriptions. To extract available multi-scale features, we design a Scaling and UpSampling (SUS) module to balance the information content between features of different scales. To capture more discriminative features, an Attentional Fully Connected (AFC) module is proposed by introducing the attention mechanism into the fully connected operations to enhance the significant informative features and suppress less useful ones. Based on the location cues, the final GA region prediction is obtained by the projection segmentation of MS-CAM. The experimental results on two independent datasets demonstrate that the proposed weakly supervised model outperforms the conventional GA segmentation methods and can produce similar or superior accuracy comparing with fully supervised approaches. The source code has been released and is available on GitHub: https://github.com/ jizexuan/Multi-Scale-Class-Activation-Map-Tensorflow.

[The expression and significance of IGF1R-Ras/RAGE-HMGB1 pathway in colorectal cancer patients with type 2 diabetes mellitus]

Objective: To investigate the expression of IGF1R-Ras and RAGE-HMGB1 signaling pathways in colorectal cancer patients with type 2 diabetes mellitus and their significance. Methods: The resected cancer tissues were obtained from 59 patients with colorectal cancer (CRC), including 29 patients with type 2 diabetes mellitus (CRC/DM group) and 30 with CRC alone (CRC group). The expressions of IGF1R, Ras, RAGE and HMGB1 in cancer tissues were detected by immunohistochemistry. The differences between the two groups were compared and the relationship between the expression and clinicopathological characteristics was analyzed. Results: In CRC/DM group, the positive rates of IGF1R and Ras were both 65.5% (19/29), and 51.7% (15/29) patients had IGF1R+ Ras+ immunophenotype, which were significantly higher than those in CRC group [33.3% (10/30), 36.7% (11/30) and 20.0% (6/30); P=0.013, 0.027 and 0.011, respectively]. The expression of IGF1R and Ras in CRC / DM group was positively correlated (r=0.479, P=0.017). The positive rate of RAGE expression in CRC group and CRC/DM group was 70.0% (21/30) and 72.4% (21/29) respectively, and the positive rate of HMGB1 expression was 46.7% (14/30) and 58.6% (17/29) respectively, neither was observed with significant difference (P=0.358 and 0.838). However, the proportion of patients with RAGE+ HMGB1+ immunophenotype in CRC/DM group [55.2% (16/29)] was higher than that in CRC Group [26.7% (8/30)] which was statistically significant (P=0.026), and the expression of both proteins was positively correlated in CRC/DM group (r=0.578, P=0.003). The clinicopathological analysis showed that in both groups the expression of IGF1R, Ras, RAGE and HMGB1 had no correlation with the sex, age, differentiation degree, tumor length, T stage and lymph node metastasis (P>0.05). Conclusion: Both IGF1R-Ras and RAGE-HMGB1 pathways may be involved in the oncogenesis of colorectal cancer in patients with type 2 diabetes.

Adaptive-Guided-Coupling-Probability Level Set for Retinal Layer Segmentation

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images.

Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging

BACKGROUND AND OBJECTIVE

Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images.

METHODS

An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data. A GA region-aware conditional generative adversarial network is employed to generate a plausible FAF image from the en-face OCT image. The network balances the consistency between the entire synthesize FAF image and the lesion. We use a fully convolutional deep network architecture to segment the GA region using the multimodal images, where the features of the en-face OCT and synthesized FAF images are fused on the front-end of the network.

RESULTS

Experimental results for 56 SD-OCT scans with GA indicate that our synthesis algorithm can generate high-quality synthesized FAF images and that the proposed segmentation network achieves a dice similarity coefficient, an overlap ratio, and an absolute area difference of 87.2%, 77.9%, and 11.0%, respectively.

CONCLUSION

We report an automatic GA segmentation method utilizing synthesized FAF images.

SIGNIFICANCE

Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.

Multi-phase level set algorithm based on fully convolutional networks (FCN-MLS) for retinal layer segmentation in SD-OCT images with central serous chorioretinopathy (CSC)

As a function of the spatial position of the optical coherence tomography (OCT) image, retinal layer thickness is an important diagnostic indicator for many retinal diseases. Reliable segmentation of the retinal layer is necessary for extracting useful clinical information. However, manual segmentation of these layers is time-consuming and prone to bias. Furthermore, due to speckle noise, low image contrast, retinal detachment, and also irregular morphological features make the automatic segmentation task challenging. To alleviate these challenges, in this paper, we propose a new coarse-fine framework combining the full convolutional network (FCN) with a multiphase level set (named FCN-MLS) for automatic segmentation of nine boundaries in retinal spectral OCT images. In the coarse stage, FCN is used to learn the characteristics of specific retinal layer boundaries and achieve classification of four retinal layers. The boundaries are then extracted and the remaining boundaries are initialized based on a priori information about the thickness of the retinal layer. In order to prevent the overlapping of the segmentation interfaces, a regional restriction technique is used in the multi-phase level to evolve the boundaries to achieve fine nine retinal layers segmentation. Experimental results on 1280 B-scans show that the proposed method can segment nine retinal boundaries accurately. Compared with the manual delineation, the overall mean absolute boundary location difference and the overall mean absolute thickness difference were 5.88 ± 2.38μm and 5.81 ± 2.19μm, which showed a good consistency with manual segmentation by the physicians. Our experimental results also outperform state-of-the-art methods.

Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images

BACKGROUND AND OBJECTIVE

Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation.

METHODS

In this paper, we propose a novel image-to-image double-branched and area-constraint fully convolutional networks (DA-FCN) for segmenting subretinal fluid in SD-OCT images. Firstly, the dataset is extended by mirroring image, which helps to overcome the over-fitting problem in the training stage. Then, double-branched structures are designed to learn the shallow coarse and deep representations from the SD-OCT images. DA-FCN model is directly trained using the image and corresponding pixel-based ground truth. Finally, we introduce a novel supervision mechanism by jointing the area loss L_A with the softmax loss L_S to learn more representative features.

RESULTS

The testing dataset with 52 SD-OCT volumes from 35 eyes of 35 patients is used for the evaluation of the proposed algorithm based on the cross-validation method. For the three criterions, including the true positive volume fraction, dice similarity coefficient, and positive predicative value, our method can obtain the results of (1) 94.3, 95.3, and 96.4 for dataset 1; (2) 97.3, 95.3, and 93.4 for dataset 2; (3) 93.0, 92.8, and 92.8 for dataset 3; (4) 89.7, 90.1, and 92.6 for dataset 4.

CONCLUSION

In this work, we propose a novel fully convolutional network for the automatic segmentation of the subretinal fluid. By constructing the double branched structures and area constraint term, our method shows higher segmentation accuracy without layer segmentation compared with other methods.

The expression of prostaglandins-related genes in erythrocytes of broiler chicken responds to thiram-induced tibial dyschondroplasia and recombinant glutathione-S-transferase A3 protein

Tibial dyschondroplasia (TD) is a type of bone deformity found in fast-growing chickens, which induce inflammatory responses. Prostaglandins (PGs) implicate in bone formation and bone resorption, associated with inflammation in an autocrine/paracrine manner. This study used qRT-PCR and immunohistochemistry analysis to identify the expression patterns of PG-related genes in the erythrocytes of broiler chickens and explore the effects of thiram-induced TD and the recombinant glutathione-S-transferase A3 (rGSTA3) protein on the expression of PG-related genes: GSTA3, cyclooxygenase 2 (COX-2), prostaglandin D2 synthase (PTGDS), prostaglandin E synthase (PTGES), prostaglandin E2 receptor (PTGER) 3, PTGER4 and prostaglandin reductase 1 (PTGR1). Interestingly, the results showed that these seven PG-related genes expression was identified in the erythrocytes of broiler chicken, and thiram-induced TD suppressed the expression of these PG-related genes in the initial stage of TD and promoted their expression in TD recovery. These findings demonstrated that the immunoregulatory function of erythrocytes can be inhibited in the early stage of TD and promoted in the recovery stage by modulating the expression of PG-related genes. Further, the rGSTA3 protein can modulate the expression of PG-related genes in erythrocytes and participate in the recovery of TD.

Automated choroid segmentation of three-dimensional SD-OCT images by incorporating EDI-OCT images

BACKGROUND AND OBJECTIVE

The measurement of choroidal volume is more related with eye diseases than choroidal thickness, because the choroidal volume can reflect the diseases comprehensively. The purpose is to automatically segment choroid for three-dimensional (3D) spectral domain optical coherence tomography (SD-OCT) images.

METHODS

We present a novel choroid segmentation strategy for SD-OCT images by incorporating the enhanced depth imaging OCT (EDI-OCT) images. The down boundary of the choroid, namely choroid-sclera junction (CSJ), is almost invisible in SD-OCT images, while visible in EDI-OCT images. During the SD-OCT imaging, the EDI-OCT images can be generated for the same eye. Thus, we present an EDI-OCT-driven choroid segmentation method for SD-OCT images, where the choroid segmentation results of the EDI-OCT images are used to estimate the average choroidal thickness and to improve the construction of the CSJ feature space of the SD-OCT images. We also present a whole registration method between EDI-OCT and SD-OCT images based on retinal thickness and Bruch's Membrane (BM) position. The CSJ surface is obtained with a 3D graph search in the CSJ feature space.

RESULTS

Experimental results with 768 images (6 cubes, 128 B-scan images for each cube) from 2 healthy persons, 2 age-related macular degeneration (AMD) and 2 diabetic retinopathy (DR) patients, and 210 B-scan images from other 8 healthy persons and 21 patients demonstrate that our method can achieve high segmentation accuracy. The mean choroid volume difference and overlap ratio for 6 cubes between our proposed method and outlines drawn by experts were -1.96µm3 and 88.56%, respectively.

CONCLUSIONS

Our method is effective for the 3D choroid segmentation of SD-OCT images because the segmentation accuracy and stability are compared with the manual segmentation.

Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images

PURPOSE

To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation.

METHODS

An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the training phase, the labeled A-scans with 1024 features were directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier was trained to determine the label of each individual pixel. Finally, a voting decision strategy was used to refine the segmentation results among 10 trained models.

RESULTS

Two image data sets with GA were used to evaluate the model. For the first dataset, our algorithm obtained a mean overlap ratio (OR) 86.94% ± 8.75%, absolute area difference (AAD) 11.49% ± 11.50%, and correlation coefficients (CC) 0.9857; for the second dataset, the mean OR, AAD, and CC of the proposed method were 81.66% ± 10.93%, 8.30% ± 9.09%, and 0.9952, respectively. The proposed algorithm was capable of improving over 5% and 10% segmentation accuracy, respectively, when compared with several state-of-the-art algorithms on two data sets.

CONCLUSIONS

Without retinal layer segmentation, the proposed algorithm could produce higher segmentation accuracy and was more stable when compared with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable GA segmentations from SD-OCT images and be useful in the clinical diagnosis of advanced nonexudative AMD.

TRANSLATIONAL RELEVANCE

Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD.

Comparison of vertical and horizontal swimming behaviour of the weather loach Misgurnus anguillicaudatus

Experiments on the swimming kinetics and behaviour of weather loach Misgurnus anguillicaudatus showed that horizontal swim speed was significantly greater than swim speeds when ascending to or descending from the water surface to gulp air. Vertical swimming speeds during ascending or descending were similar. Misgurnus anguillicaudatus swam unsteadily during vertical movements compared with horizontal movements.

Quantitative Analysis of Unidirectional 2-D Tissue Formation of Endothelial Cells

We developed a reliable and quantitative method for measuring the dynamic process of unidirectional two-dimensional (2-D) tissue formation of endothelial cells (ECs) in vitro. The culturing of bovine ECs in an assembled culture chamber provided a square monolayered cell sheet with a linear margin when disassembled at the confluency. The cell sheet maintained in culture showed a unidirectional endothelialization in vitro. The cell population-distance histogram, which was determined from the daily observation of tissue, allowed us to determine quantitatively the dynamic process of unidirectional endothelialization in vitro. The endothelialized distance and the endothelializing zone on a glass slide were found to be nearly 500 μm/day and 750 μm in width, respectively. Thus, the method developed here provided information of the 2-D tissue formation process. This model would be useful as an in vitro model which simulates the anastomotic endothelialization of an artificial vascular graft.