A Bis-Cyclopentadienyl Ligand-Supported Di-Iron Trihydride Motif as a Synthon for Access to Heterobimetallic Trinuclear Complexes.

Herein, we report the synthesis of a flexible bis-cyclopentadienyl ligand L (the doubly deprotonated form of H2L (1,3-bis(2,4-di-tert-butylcyclopentadienyldimethylsilyl)benzene)), demonstrating its ability to stabilize a series of di-iron hydrido complexes. Notably, this ligand facilitates the isolation of an unprecedented anionic cyclopentadienyl ligand-supported di-iron trihydride complex, LFe2(µ-H)3Li(THF) (2), functioning as a synthon for the [Fe2(µ-H)3]- core and providing access to heterobimetallic complexes 4-6 with coinage metals.

Accurate C/D ratio estimation with elliptical fitting for OCT image based on joint segmentation and detection network.

Proper estimation of the cup-to-disc ratio (C/D ratio) plays a significant role in ophthalmic examinations, and it is urgent to improve the efficiency of C/D ratio automatic measurement. Therefore, we propose a new method for measuring the C/D ratio of OCTs in normal subjects. Firstly, the end-to-end deep convolution network is used to segment and detect the inner limiting membrane (ILM) and the two Bruch's membrane opening (BMO) terminations. Then, we introduce an ellipse fitting technique to post-process the edge of the optic disc. Finally, the proposed method is evaluated on 41 normal subjects using the optic-disc-area scanning mode of three machines: BV1000, Topcon 3D OCT-1, and Nidek ARK-1. In addition, pairwise correlation analyses are carried out to compare the C/D ratio measurement method of BV1000 to existing commercial OCT machines as well as other state-of-the-art methods. The correlation coefficient between the C/D ratio calculated by BV1000 and the C/D ratio calculated by manual annotation is 0.84, which indicates that the proposed method has a strong correlation with the results of manual annotation by ophthalmologists. Moreover, in comparison between BV1000, Topcon and Nidek in practical screening among normal subjects, the proportion of the C/D ratio less than 0.6 calculated by BV1000 accounts for 96.34%, which is the closest to the clinical statistics among the three OCT machines. The above experimental results and analysis show that the proposed method performs well in cup and disc detection and C/D ratio measurement, and compared with the existing commercial OCT equipment, the C/D ratio measurement results are relatively close to reality, which has certain clinical application value.

Self-Guided Optimization Semi-Supervised Method for Joint Segmentation of Macular Hole and Cystoid Macular Edema in Retinal OCT Images.

Macular hole (MH) and cystoid macular edema (CME) are two common retinal pathologies that cause vision loss. Accurate segmentation of MH and CME in retinal OCT images can greatly aid ophthalmologists to evaluate the relevant diseases. However, it is still challenging as the complicated pathological features of MH and CME in retinal OCT images, such as the diversity of morphologies, low imaging contrast, and blurred boundaries. In addition, the lack of pixel-level annotation data is one of the important factors that hinders the further improvement of segmentation accuracy. Focusing on these challenges, we propose a novel self-guided optimization semi-supervised method termed Semi-SGO for joint segmentation of MH and CME in retinal OCT images. Aiming to improve the model's ability to learn the complicated pathological features of MH and CME, while alleviating the feature learning tendency problem that may be caused by the introduction of skip-connection in U-shaped segmentation architecture, we develop a novel dual decoder dual-task fully convolutional neural network (D3T-FCN). Meanwhile, based on our proposed D3T-FCN, we introduce a knowledge distillation technique to further design a novel semi-supervised segmentation method called Semi-SGO, which can leverage unlabeled data to further improve the segmentation accuracy. Comprehensive experimental results show that our proposed Semi-SGO outperforms other state-of-the-art segmentation networks. Furthermore, we also develop an automatic method for measuring the clinical indicators of MH and CME to validate the clinical significance of our proposed Semi-SGO. The code will be released on Github 1,2.

Multi-scale and local feature guidance network for corneal nerve fiber segmentation.

Objective. Corneal confocal microscopy (CCM) is a rapid and non-invasive ophthalmic imaging technique that can reveal corneal nerve fiber. The automatic segmentation of corneal nerve fiber in CCM images is vital for the subsequent abnormality analysis, which is the main basis for the early diagnosis of degenerative neurological systemic diseases such as diabetic peripheral neuropathy.Approach. In this paper, a U-shape encoder-decoder structure based multi-scale and local feature guidance neural network (MLFGNet) is proposed for the automatic corneal nerve fiber segmentation in CCM images. Three novel modules including multi-scale progressive guidance (MFPG) module, local feature guided attention (LFGA) module, and multi-scale deep supervision (MDS) module are proposed and applied in skip connection, bottom of the encoder and decoder path respectively, which are designed from both multi-scale information fusion and local information extraction perspectives to enhance the network's ability to discriminate the global and local structure of nerve fibers. The proposed MFPG module solves the imbalance between semantic information and spatial information, the LFGA module enables the network to capture attention relationships on local feature maps and the MDS module fully utilizes the relationship between high-level and low-level features for feature reconstruction in the decoder path.Main results. The proposed MLFGNet is evaluated on three CCM image Datasets, the Dice coefficients reach 89.33%, 89.41%, and 88.29% respectively.Significance. The proposed method has excellent segmentation performance for corneal nerve fibers and outperforms other state-of-the-art methods.

LKG-Net: lightweight keratoconus grading network based on corneal topography.

Keratoconus (KC) is a noninflammatory ectatic disease characterized by progressive thinning and an apical cone-shaped protrusion of the cornea. In recent years, more and more researchers have been committed to automatic and semi-automatic KC detection based on corneal topography. However, there are few studies about the severity grading of KC, which is particularly important for the treatment of KC. In this work, we propose a lightweight KC grading network (LKG-Net) for 4-level KC grading (Normal, Mild, Moderate, and Severe). First of all, we use depth-wise separable convolution to design a novel feature extraction block based on the self-attention mechanism, which can not only extract rich features but also reduce feature redundancy and greatly reduce the number of parameters. Then, to improve the model performance, a multi-level feature fusion module is proposed to fuse features from the upper and lower levels to obtain more abundant and effective features. The proposed LKG-Net was evaluated on the corneal topography of 488 eyes from 281 people with 4-fold cross-validation. Compared with other state-of-the-art classification methods, the proposed method achieves 89.55% for weighted recall (W_R), 89.98% for weighted precision (W_P), 89.50% for weighted F1 score (W_F1) and 94.38% for Kappa, respectively. In addition, the LKG-Net is also evaluated on KC screening, and the experimental results show the effectiveness.

OCT2Former: A retinal OCT-angiography vessel segmentation transformer.

BACKGROUND AND OBJECTIVE: Retinal vessel segmentation plays an important role in the automatic retinal disease screening and diagnosis. How to segment thin vessels and maintain the connectivity of vessels are the key challenges of the retinal vessel segmentation task. Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. Aiming at make full use of its characteristic of high resolution, a new end-to-end transformer based network named as OCT2Former (OCT-a Transformer) is proposed to segment retinal vessel accurately in OCTA images. METHODS: The proposed OCT2Former is based on encoder-decoder structure, which mainly includes dynamic transformer encoder and lightweight decoder. Dynamic transformer encoder consists of dynamic token aggregation transformer and auxiliary convolution branch, in which the multi-head dynamic token aggregation attention based dynamic token aggregation transformer is designed to capture the global retinal vessel context information from the first layer throughout the network and the auxiliary convolution branch is proposed to compensate for the lack of inductive bias of the transformer and assist in the efficient feature extraction. A convolution based lightweight decoder is proposed to decode features efficiently and reduce the complexity of the proposed OCT2Former. RESULTS: The proposed OCT2Former is validated on three publicly available datasets i.e. OCTA-SS, ROSE-1, OCTA-500 (subset OCTA-6M and OCTA-3M). The Jaccard indexes of the proposed OCT2Former on these datasets are 0.8344, 0.7855, 0.8099 and 0.8513, respectively, outperforming the best convolution based network 1.43, 1.32, 0.75 and 1.46%, respectively. CONCLUSION: The experimental results have demonstrated that the proposed OCT2Former can achieve competitive performance on retinal OCTA vessel segmentation tasks.

Morphological prognosis prediction of choroid neovascularization from longitudinal SD-OCT images.

BACKGROUND: Choroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment. PURPOSE: Accurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment. METHODS: In this paper, we develop a CNN-based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD-OCT) B-scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co-segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow-up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder-recurrent-decoder framework is developed for CNV growth prediction, in which an attention-based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations. RESULTS: The registration network rigidly registers the follow-up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state-of-the-art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow-up visit. CONCLUSION: The proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

ADS-Net: attention-awareness and deep supervision based network for automatic detection of retinopathy of prematurity.

Retinopathy of prematurity (ROP) is a proliferative vascular disease, which is one of the most dangerous and severe ocular complications in premature infants. Automatic ROP detection system can assist ophthalmologists in the diagnosis of ROP, which is safe, objective, and cost-effective. Unfortunately, due to the large local redundancy and the complex global dependencies in medical image processing, it is challenging to learn the discriminative representation from ROP-related fundus images. To bridge this gap, a novel attention-awareness and deep supervision based network (ADS-Net) is proposed to detect the existence of ROP (Normal or ROP) and 3-level ROP grading (Mild, Moderate, or Severe). First, to balance the problems of large local redundancy and complex global dependencies in images, we design a multi-semantic feature aggregation (MsFA) module based on self-attention mechanism to take full advantage of convolution and self-attention, generating attention-aware expressive features. Then, to solve the challenge of difficult training of deep model and further improve ROP detection performance, we propose an optimization strategy with deeply supervised loss. Finally, the proposed ADS-Net is evaluated on ROP screening and grading tasks with per-image and per-examination strategies, respectively. In terms of per-image classification pattern, the proposed ADS-Net achieves 0.9552 and 0.9037 for Kappa index in ROP screening and grading, respectively. Experimental results demonstrate that the proposed ADS-Net generally outperforms other state-of-the-art classification networks, showing the effectiveness of the proposed method.

An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics.

Objectives: To develop and validate an efficient and automatically computational approach for stratifying glioma grades and predicting survival of lower-grade glioma (LGG) patients using an integration of state-of-the-art convolutional neural network (CNN) and radiomics. Method: This retrospective study reviewed 470 preoperative MR images of glioma from BraTs public dataset (n=269) and Jinling hospital (n=201). A fully automated pipeline incorporating tumor segmentation and grading was developed, which can avoid variability and subjectivity of manual segmentations. First, an integrated approach by fusing CNN features and radiomics features was employed to stratify glioma grades. Then, a deep-radiomics signature based on the integrated approach for predicting survival of LGG patients was developed and subsequently validated in an independent cohort. Results: The performance of tumor segmentation achieved a Dice coefficient of 0.81. The intraclass correlation coefficients (ICCs) of the radiomics features between the segmentation network and physicians were all over 0.75. The performance of glioma grading based on integrated approach achieved the area under the curve (AUC) of 0.958, showing the effectiveness of the integrated approach. The multivariable Cox regression results demonstrated that the deep-radiomics signature remained an independent prognostic factor and the integrated nomogram showed significantly better performance than the clinical nomogram in predicting overall survival of LGG patients (C-index: 0.865 vs. 0.796, P=0.005). Conclusion: The proposed integrated approach can be noninvasively and efficiently applied in prediction of gliomas grade and survival. Moreover, our fully automated pipeline successfully achieved computerized segmentation instead of manual segmentation, which shows the potential to be a reproducible approach in clinical practice.

DHNet: High-resolution and hierarchical network for cross-domain OCT speckle noise reduction.

PURPOSE: Optical coherence tomography (OCT) imaging uses the principle of Michelson interferometry to obtain high-resolution images by coherent superposing of multiple forward and backward scattered light waves with random phases. This process inevitably produces speckle noise that severely compromises visual quality of OCT images and degrades performances of subsequent image analysis tasks. In addition, datasets obtained by different OCT scanners have distribution shifts, making a speckle noise suppression model difficult to be generalized across multiple datasets. In order to solve the above issues, we propose a novel end-to-end denoising framework for OCT images collected by different scanners. METHODS: The proposed model utilizes the high-resolution network (HRNet) as backbone for image restoration, which reconstructs high-fidelity images by maintaining high-resolution representations throughout the entire learning process. To compensate distribution shifts among datasets collected by different scanners, we develop a hierarchical adversarial learning strategy for domain adaption. The proposed model is trained using datasets with clean ground truth produced by two commercial OCT scanners, and then applied to suppress speckle noise in OCT images collected by our recently developed OCT scanner, BV-1000 (China Bigvision Corporation). We name the proposed model as DHNet (Double-H-Net, High-resolution and Hierarchical Network). RESULTS: We compare DHNet with state-of-the-art methods and experiment results show that DHNet improves signal-to-noise ratio by a large margin of 18.137 dB as compared to the best of our previous method. In addition, DHNet achieves a testing time of 25 ms, which satisfies the real-time processing requirement for the BV-1000 scanner. We also conduct retinal layer segmentation experiment on OCT images before and after denoising and show that DHNet can also improve segmentation. CONCLUSIONS: The proposed DHNet can compensate domain shifts between different data sets while significantly improve speckle noise suppression. The HRNet backbone is utilized to carry low- and high-resolution information to recover fidelity images. Domain adaptation is achieved by a hierarchical module through adversarial learning. In addition, DHNet achieved a testing time of 25 ms, which satisfied the real-time processing requirement.

AFENet: Attention Fusion Enhancement Network for Optic Disc Segmentation of Premature Infants.

Retinopathy of prematurity and ischemic brain injury resulting in periventricular white matter damage are the main causes of visual impairment in premature infants. Accurate optic disc (OD) segmentation has important prognostic significance for the auxiliary diagnosis of the above two diseases of premature infants. Because of the complexity and non-uniform illumination and low contrast between background and the target area of the fundus images, the segmentation of OD for infants is challenging and rarely reported in the literature. In this article, to tackle these problems, we propose a novel attention fusion enhancement network (AFENet) for the accurate segmentation of OD in the fundus images of premature infants by fusing adjacent high-level semantic information and multiscale low-level detailed information from different levels based on encoder-decoder network. Specifically, we first design a dual-scale semantic enhancement (DsSE) module between the encoder and the decoder inspired by self-attention mechanism, which can enhance the semantic contextual information for the decoder by reconstructing skip connection. Then, to reduce the semantic gaps between the high-level and low-level features, a multiscale feature fusion (MsFF) module is developed to fuse multiple features of different levels at the top of encoder by using attention mechanism. Finally, the proposed AFENet was evaluated on the fundus images of preterm infants for OD segmentation, which shows that the proposed two modules are both promising. Based on the baseline (Res34UNet), using DsSE or MsFF module alone can increase Dice similarity coefficients by 1.51 and 1.70%, respectively, whereas the integration of the two modules together can increase 2.11%. Compared with other state-of-the-art segmentation methods, the proposed AFENet achieves a high segmentation performance.

Automatic zoning for retinopathy of prematurity with semi-supervised feature calibration adversarial learning.

Retinopathy of prematurity (ROP) is an eye disease, which affects prematurely born infants with low birth weight and is one of the main causes of children's blindness globally. In recent years, there are many studies on automatic ROP diagnosis, mainly focusing on ROP screening such as "Yes/No ROP" or "Mild/Severe ROP" and presence/absence detection of "plus disease". Due to the lack of corresponding high-quality annotations, there are few studies on ROP zoning, which is one of the important indicators to evaluate the severity of ROP. Moreover, how to effectively utilize the unlabeled data to train model is also worth studying. Therefore, we propose a novel semi-supervised feature calibration adversarial learning network (SSFC-ALN) for 3-level ROP zoning, which consists of two subnetworks: a generative network and a compound network. The generative network is a U-shape network for producing the reconstructed images and its output is taken as one of the inputs of the compound network. The compound network is obtained by extending a common classification network with a discriminator, introducing adversarial mechanism into the whole training process. Because the definition of ROP tells us where and what to focus on in the fundus images, which is similar to the attention mechanism. Therefore, to further improve classification performance, a new attention mechanism based feature calibration module (FCM) is designed and embedded in the compound network. The proposed method was evaluated on 1013 fundus images of 108 patients with 3-fold cross validation strategy. Compared with other state-of-the-art classification methods, the proposed method achieves high classification performance.

Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks.

Objective. Retinal fluid mainly includes intra-retinal fluid (IRF), sub-retinal fluid (SRF) and pigment epithelial detachment (PED), whose accurate segmentation in optical coherence tomography (OCT) image is of great importance to the diagnosis and treatment of the relative fundus diseases.Approach. In this paper, a novel two-stage multi-class retinal fluid joint segmentation framework based on cascaded convolutional neural networks is proposed. In the pre-segmentation stage, a U-shape encoder-decoder network is adopted to acquire the retinal mask and generate a retinal relative distance map, which can provide the spatial prior information for the next fluid segmentation. In the fluid segmentation stage, an improved context attention and fusion network based on context shrinkage encode module and multi-scale and multi-category semantic supervision module (named as ICAF-Net) is proposed to jointly segment IRF, SRF and PED.Main results. the proposed segmentation framework was evaluated on the dataset of RETOUCH challenge. The average Dice similarity coefficient, intersection over union and accuracy (Acc) reach 76.39%, 64.03% and 99.32% respectively.Significance. The proposed framework can achieve good performance in the joint segmentation of multi-class fluid in retinal OCT images and outperforms some state-of-the-art segmentation networks.

An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks.

Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature.

Global and Local Feature Reconstruction for Medical Image Segmentation.

Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate these two problems, but the global feature extraction ability and spatial information recovery ability of U-Net are still insufficient. In this paper, we propose a Global Feature Reconstruction (GFR) module to efficiently capture global context features and a Local Feature Reconstruction (LFR) module to dynamically up-sample features, respectively. For the GFR module, we first extract the global features with category representation from the feature map, then use the different level global features to reconstruct features at each location. The GFR module establishes a connection for each pair of feature elements in the entire space from a global perspective and transfers semantic information from the deep layers to the shallow layers. For the LFR module, we use low-level feature maps to guide the up-sampling process of high-level feature maps. Specifically, we use local neighborhoods to reconstruct features to achieve the transfer of spatial information. Based on the encoder-decoder architecture, we propose a Global and Local Feature Reconstruction Network (GLFRNet), in which the GFR modules are applied as skip connections and the LFR modules constitute the decoder path. The proposed GLFRNet is applied to four different medical image segmentation tasks and achieves state-of-the-art performance.

Intravenous Immunoglobulin Therapy Restores the Quantity and Phenotype of Circulating Dendritic Cells and CD4+ T Cells in Children With Acute Kawasaki Disease.

Background: Intravenous immunoglobulin (IVIG) showed its therapeutic efficacy on Kawasaki disease (KD). However, the mechanisms by which it reduces systemic inflammation are not completely understood. Dendritic cells (DCs) and T cells play critical roles in the pathogenic processes of immune disorders. Assessing the quantity of DC subsets and T cells and identifying functional molecules present on these cells, which provide information about KD, in the peripheral blood may provide new insights into the mechanisms of immunoglobulin therapy. Methods: In total, 54 patients with KD and 27 age-matched healthy controls (HCs) were included in this study. The number, percentage, and phenotype of DC subsets and CD4+ T cells in peripheral blood were analyzed through flow cytometry. Results: Patients with KD exhibited fewer peripheral DC subsets and CD4+ T cells than HCs. Human leucocyte antigen-DR (HLA-DR) expression was reduced on CD1c+ myeloid DCs (CD1c+ mDCs), whereas that on plasmacytoid DCs (pDCs) did not change significantly. Both pDCs and CD1c+ mDCs displayed significantly reduced expression of co-stimulatory molecules, including CD40, CD86. pDCs and CD1c+ mDCs presented an immature or tolerant phenotype in acute stages of KD. Number of circulating pDC and CD1c+ mDC significantly inversely correlated with plasma interleukin-6 (IL-6) levels in KD patients pre-IVIG treatment. No significant differences were found concerning the DC subsets and CD4+ T cells in patients with KD with and without coronary artery lesions. Importantly, these altered quantity and phenotypes on DC subsets and CD4+ T cells were restored to a great extent post-IVIG treatment. T helper (Th) subsets including Th1 and Th2 among CD4+ T cells did not show alteration pre- and post-IVIG treatment, although the Th1-related cytokine IFN-γ level in plasma increased dramatically in patients with KD pre-IVIG treatment. Conclusions: pDCs and CD1c+ mDCs presented an immature or tolerant phenotype in acute stages of KD, IVIG treatment restored the quantity and functional molecules of DCs and CD4+ T cells to distinct levels in vivo, indicating the involvement of DCs and CD4+ T cells in the inflammation in KD. The findings provide insights into the immunomodulatory actions of IVIG in KD.

Sugarcane cellulose-based composite hydrogel enhanced by g-C3N4 nanosheet for selective removal of organic dyes from water.

The effective removal of toxic dyes from aqueous solution is of great significance for environmental protection. Herein, an eco-friendly sugarcane cellulose (SBC)/sodium carboxymethylcellulose (CMC-Na) adsorbent reinforced with carbon nitride (g-C3N4) was successfully prepared via a facile sol-gel method. The resulting gel-like adsorbent or composite hydrogel was comprehensively characterized with FTIR, SEM, EDS, TGA analysis. The adsorption behaviors of the adsorbent in the removal of methylene blue (MB) were systematically investigated. Results showed the pseudo-second-order kinetic model and Langmuir model described adsorption process accurately with the maximum adsorption capacity of 362.3 mg g-1, indicating that adsorption behavior is a monolayer chemical adsorption. Moreover, the composited hydrogel displayed excellent adsorption selectivity on MB/MO or MB/RhB mixed dyes. In addition, adsorbent showed great stability and reusability with almost no loss in adsorption capacity after 7 cycles. Due to the facile preparation process and outstanding mechanical properties, as well as high recyclability, g-C3N4@SBC/CMC has great potential in wastewater treatment.

Multi-Discriminator Adversarial Convolutional Network for Nerve Fiber Segmentation in Confocal Corneal Microscopy Images.

Quantitative measurements of corneal sub-basal nerves are biomarkers for many ocular surface disorders and are also important for early diagnosis and assessment of progression of neurodegenerative diseases. This paper aims to develop an automatic method for nerve fiber segmentation from in vivo corneal confocal microscopy (CCM) images, which is fundamental for nerve morphology quantification. A novel multi-discriminator adversarial convolutional network (MDACN) is proposed, where both the generator and the two discriminators emphasize multi-scale feature representations. The generator is a U-shaped fully convolutional network with multi-scale split and concatenate blocks, and the two discriminators have different effective receptive fields, sensitive to features of different scales. A novel loss function is also proposed which enables the network to pay more attention to thin fibers. The MDACN framework was evaluated on four datasets. Experiment results show that our method has excellent segmentation performance for corneal nerve fibers and outperforms some state-of-the-art methods.

Speckle Noise Reduction for OCT Images Based on Image Style Transfer and Conditional GAN.

Raw optical coherence tomography (OCT) images typically are of low quality because speckle noise blurs retinal structures, severely compromising visual quality and degrading performances of subsequent image analysis tasks. In our previous study (Ma et al., 2018), we have developed a Conditional Generative Adversarial Network (cGAN) for speckle noise removal in OCT images collected by several commercial OCT scanners, which we collectively refer to as scanner T. In this paper, we improve the cGAN model and apply it to our in-house OCT scanner (scanner B) for speckle noise suppression. The proposed model consists of two steps: 1) We train a Cycle-Consistent GAN (CycleGAN) to learn style transfer between two OCT image datasets collected by different scanners. The purpose of the CycleGAN is to leverage the ground truth dataset created in our previous study. 2) We train a mini-cGAN model based on the PatchGAN mechanism with the ground truth dataset to suppress speckle noise in OCT images. After training, we first apply the CycleGAN model to convert raw images collected by scanner B to match the style of the images from scanner T, and subsequently use the mini-cGAN model to suppress speckle noise in the style transferred images. We evaluate the proposed method on a dataset collected by scanner B. Experimental results show that the improved model outperforms our previous method and other state-of-the-art models in speckle noise removal, retinal structure preservation and contrast enhancement.

Joint Segmentation of Multi-Class Hyper-Reflective Foci in Retinal Optical Coherence Tomography Images.

Hyper-reflective foci (HRF) refers to the spot-shaped, block-shaped areas with characteristics of high local contrast and high reflectivity, which is mostly observed in retinal optical coherence tomography (OCT) images of patients with fundus diseases. HRF mainly appears hard exudates (HE) and microglia (MG) clinically. Accurate segmentation of HE and MG is essential to alleviate the harm in retinal diseases. However, it is still a challenge to segment HE and MG simultaneously due to similar pathological features, various shapes and location distribution, blurred boundaries, and small morphology dimensions. To tackle these problems, in this paper, we propose a novel global information fusion and dual decoder collaboration-based network (GD-Net), which can segment HE and MG in OCT images jointly. Specifically, to suppress the interference of similar pathological features, a novel global information fusion (GIF) module is proposed, which can aggregate the global semantic information efficiently. To further improve the segmentation performance, we design a dual decoder collaborative workspace (DDCW) to comprehensively utilize the semantic correlation between HE and MG while enhancing the mutual influence on them by feedback alternately. To further optimize GD-Net, we explore a joint loss function which integrates pixel-level with image-level. The dataset of this study comes from patients diagnosed with diabetic macular edema at the department of ophthalmology, University Medical Center Groningen, The Netherlands. Experimental results show that our proposed method performs better than other state-of-the-art methods, which suggests the effectiveness of the proposed method and provides research ideas for medical applications.