Retinal Vessel Segmentation via Adversarial Learning and Iterative Refinement

Retinal vessel segmentation is a challenging medical task owing to small size of dataset,micro blood vessels and low image contrast.To address these issues,we introduce a novel convolutional neural network in this paper,which takes the advantage of both adversarial learning and recurrent neural network.An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually.Recurrent unit preserves high-level semantic information for feature reuse,so as to output a sufficiently refined segmentation map instead of a coarse mask.Moreover,an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions,thus greatly reducing topology errors of segmentation.The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17%and 80.64%,respectively.Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.

An iterative refinement method combining detector geometry optimization and diffraction model refinement in serial femtosecond crystallography

Background Recent advances in serial femtosecond crystallography(SFX)using X-ray free electron lasers(XFELs)have facilitated accurate structure determination for biological macromolecules.However,given the many fluctuations inherent in SFX,the acquisition of SFX data of sufficiently high quality still remains challenging.Method Aimed at enhancing the accuracy of SFX data,this study proposes an iterative refinement method to optimally match pairs of the observed and predicted reflections on the detector plane.This method features a combination of detector geometry optimization and diffraction model refinement in an alternate manner,concomitant with a cycle-by-cycle peak selection procedure.Result To demonstrate whether this iterative method is convergent and feasible,both numerical simulations and experimental tests have been performed.The results reveal that this method can gradually improve overall quality of the integrated SFX data and therefore accelerate the convergence of Monte Carlo integration,while simultaneously suppressing correlations inherent in certain parameters and precluding outliers to some extent during the refinement.Conclusion We have demonstrated that our iterative refinement method is applicable to both simulated and experimental SFX data.It is expected that this method could provide meaningful insights into the refinement of SFX data and take the step forward toward more accurate Monte Carlo integration.