CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation.

Precise segmentation of retinal vessels plays an important role in computer-assisted diagnosis. Deep learning models have been applied to retinal vessel segmentation, but the efficacy is limited by the significant scale variation of vascular structures and the intricate background of retinal images. This paper supposes a cross-channel spatial attention U-Net (CCS-UNet) for accurate retinal vessel segmentation. In comparison to other models based on U-Net, our model employes a ResNeSt block for the encoder-decoder architecture. The block has a multi-branch structure that enables the model to extract more diverse vascular features. It facilitates weight distribution across channels through the incorporation of soft attention, which effectively aggregates contextual information in vascular images. Furthermore, we suppose an attention mechanism within the skip connection. This mechanism serves to enhance feature integration across various layers, thereby mitigating the degradation of effective information. It helps acquire cross-channel information and enhance the localization of regions of interest, ultimately leading to improved recognition of vascular structures. In addition, the feature fusion module (FFM) module is used to provide semantic information for a more refined vascular segmentation map. We evaluated CCS-UNet based on five benchmark retinal image datasets, DRIVE, CHASEDB1, STARE, IOSTAR and HRF. Our proposed method exhibits superior segmentation efficacy compared to other state-of-the-art techniques with a global accuracy of 0.9617/0.9806/0.9766/0.9786/0.9834 and AUC of 0.9863/0.9894/0.9938/0.9902/0.9855 on DRIVE, CHASEDB1, STARE, IOSTAR and HRF respectively. Ablation studies are also performed to evaluate the the relative contributions of different architectural components. Our proposed model is potential for diagnostic aid of retinal diseases.

Deep learning-based automated detection of retinal diseases using optical coherence tomography images.

Retinal disease classification is a significant problem in computer-aided diagnosis (CAD) for medical applications. This paper is focused on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The proposed classification algorithm adopted an ensemble of four classification model instances to identify retinal OCT images, each of which was based on an improved residual neural network (ResNet50). The experiment followed a patient-level 10-fold cross-validation process, on development retinal OCT image dataset. The proposed approach achieved 0.973 (95% confidence interval [CI], 0.971-0.975) classification accuracy, 0.963 (95% CI, 0.960-0.966) sensitivity, and 0.985 (95% CI, 0.983-0.987) specificity at the B-scan level, achieving a matching or exceeding performance to that of ophthalmologists with significant clinical experience. Other performance measures used in the study were the area under receiver operating characteristic curve (AUC) and kappa value. The observations of the study implied that multi-ResNet50 ensembling was a useful technique when the availability of medical images was limited. In addition, we performed qualitative evaluation of model predictions, and occlusion testing to understand the decision-making process of our model. The paper provided an analytical discussion on misclassification and pathology regions identified by the occlusion testing also. Finally, we explored the effect of the integration of retinal OCT images and medical history data from patients on model performance.

Rapid microscope auto-focus method for uneven surfaces based on image fusion.

Microscopic imaging of uneven surfaces is difficult because of the limited depth of field. In this study, we developed a rapid auto-focus method for uneven surfaces based on image fusion. The Prewitt operator was used to detect the vertical edges of the images. Then, the focus position was theoretically calculated using a Gaussian function, and image fusion was applied to obtain the final in-focus image. An experiment was designed to verify the developed method. The results revealed that this method is effective for printed circuit boards.

Refractive index redistribution with accommodation based on finite volume-constant age-dependent mechanical modeling.

The human lens is considered to have a gradient refractive index (GRIN) distribution. The recently developed accommodating volume-constant age-dependent optical (AVOCADO) model can accurately describe the separate GRIN distributions in the axial and radial directions. Our study uses a finite element method to simulate the accommodation process and calculate the GRIN redistribution based on the AVOCADO model for 25-, 35- and 45-year-old lenses. The parameter p describes the steepness of the GRIN profile towards the lens periphery. The results show that axial p values increase with age. Under accommodation, the axial p value increases, while the radial p value decreases. We also use a ray tracing method to evaluate the optical performance of the lens. The aim of this paper is thus to provide an anatomically finite mechanical lens model with separate axial and radial refractive index profiles for a better understanding of accommodation at different ages.

Comparison of Choroidal Thickness in Preeclamptic, Healthy Pregnant, and Nonpregnant Women: A Systematic Review and Meta-Analysis.

PURPOSE: To compare the choroidal thickness (CT) measured by enhanced depth imaging optical coherence tomography (EDI-OCT) in preeclamptic, healthy pregnant, and healthy nonpregnant women. METHODS: Studies that focused on the CT evaluation of pregnant women were retrieved by searching PubMed, Embase, Ovid, Cochrane, and Web of Science. We used Stata 14.0 SE for the meta-analysis and presented the results as the weighted mean difference (WMD) with a corresponding 95% CI. RESULTS: A total of 14 studies with 1,227 participants were included in our meta-analysis. The CT of the healthy pregnancies (µm, WMD = 34.19, 95% CI: 20.63-47.76) was significantly higher than that of the nonpregnancies (Test of WMD = 0: z = 4.94, p = 0.000), but the CT of the preeclampsia (µm, WMD = 54.30, 95% CI: -13.40 to 122.01) was not significantly different from the nonpregnancies (Test of WMD = 0: z = 1.57, p = 0.116). In the preeclampsia versus healthy pregnancy group, 3 studies found that the choroid was thinner with preeclampsia, only one study found the CT increased. CONCLUSIONS: This meta-analysis suggested that the CT of the healthy pregnant women was significantly higher than that of the nonpregnant women. The presence of preeclampsia might complicate this situation. Most studies found that the CT decreased in the preeclamptic patients because of the increases in the systemic vasospasm and the blood pressure, which led to no significant difference compared with the nonpregnant women.

Corneal Biomechanical Properties After Penetrating Keratoplasty or Deep Anterior Lamellar Keratoplasty Using the Ocular Response Analyzer: A Meta-Analysis.

PURPOSE: To evaluate the corneal biomechanical properties of patients who have undergone penetrating keratoplasty (PK) or deep anterior lamellar keratoplasty (DALK) using the ocular response analyzer. METHODS: Stata 13.0 SE was used for this meta-analysis. Studies in the literature that focused on corneal hysteresis (CH) or corneal resistance factor (CRF) after PK or DALK were retrieved by searching PubMed, Embase, Ovid, and Cochrane databases. We present the results as weighted mean difference (WMD) with a corresponding 95% confidence interval (CI). RESULTS: Eight studies with a total of 750 eyes were included in the post-PK versus control group, and 4 studies with a total of 218 eyes were included in the post-DALK versus control group. The pooled results showed that CH and CRF were significantly reduced (P < 0.00001) for patients who have undergone PK (WMD = -1.16, 95% CI: -1.73 to -0.60 and WMD = -1.00, 95% CI: -1.61 to -0.40). No significant differences were found in both CH and CRF for patients who have undergone DALK (WMD = -0.27, 95% CI: -0.64 to -0.09 and WMD = -0.15, 95% CI: -0.53 to 0.23). CONCLUSIONS: This meta-analysis suggested that both CH and CRF had better recovery after corneal transplantation with DALK than PK.

Corneal confocal microscopy for assessment of diabetic peripheral neuropathy: a meta-analysis.

PURPOSE: To evaluate the diagnostic performance of corneal confocal microscopy (CCM) in assessing corneal nerve parameters in patients with diabetic peripheral neuropathy (DPN). METHODS: Studies in the literature that focused on CCM and DPN were retrieved by searching PubMed, Excerpt Medica Database (EMBASE) and China National Knowledge Infrastructure (CNKI) databases. RevMan V.5.3 software was used for the meta-analysis. The results are presented as weighted mean difference (WMD) with a corresponding 95% CI. RESULTS: 13 studies with a total of 1680 participants were included in the meta-analysis. The pooled results showed that the corneal nerve fibre density, nerve branch density and nerve fibre length were significantly reduced (all p<0.00001) in the patients with DPN compared with healthy controls ((WMD=-18.07, 95% CI -21.93 to -14.20), (WMD=-25.35, 95% CI -30.96 to -19.74) and (WMD=-6.37, 95% CI -7.44 to -5.30)) and compared with the diabetic patients without DPN ((WMD=-8.83, 95% CI -11.49 to -6.17), (WMD=-13.54, 95% CI -20.41 to -6.66) and (WMD=-4.19, 95% CI -5.35 to -3.04)), respectively. No significant difference was found in the corneal nerve fibre tortuosity coefficient between diabetic patients with DPN and healthy controls (p=0.80) or diabetic patients without DPN (p=0.61). CONCLUSIONS: This meta-analysis suggested that CCM may be valuable for detecting and assessing early nerve damage in DPN patients.