Author Affiliations
Abstract
1 Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, P. R. China
2 School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China Hefei 230026, P. R. China
3 Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P. R. China
The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.
Fundus fluorescein angiography image fundus structure image image translation unified deep learning model generative adversarial networks Journal of Innovative Optical Health Sciences
2024, 17(3): 2450003
Author Affiliations
Abstract
School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150000, P. R. China
Photoacoustic imaging (PAI) is a noninvasive emerging imaging method based on the photoacoustic effect, which provides necessary assistance for medical diagnosis. It has the characteristics of large imaging depth and high contrast. However, limited by the equipment cost and reconstruction time requirements, the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed. In this paper, a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data. Specifically, the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data, and takes the photoacoustic physical model as a prior information to guide the reconstruction process. In addition, to enhance the ability of extracting signal features, the residual block and squeeze and excitation block are introduced into the TFT-Net. For further efficient reconstruction, the final output of photoacoustic signals uses ‘filter-then-upsample’ operation with a pixel-shuffle multiplexer and a max out module. Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly, reduce background noise, and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.
Deep learning feature transformation image reconstruction limited-view measurement photoacoustic tomography Journal of Innovative Optical Health Sciences
2024, 17(3): 2350028
Author Affiliations
Abstract
1 Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Tianjin 300350, China
2 Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
Limited by the dynamic range of the detector, saturation artifacts usually occur in optical coherence tomography (OCT) imaging for high scattering media. The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images. We proposed a deep learning-based inpainting method of saturation artifacts in this paper. The generation mechanism of saturation artifacts was analyzed, and experimental and simulated datasets were built based on the mechanism. Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs. The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility, strong generalization, and robustness.
Optical coherence tomography saturation artifacts deep learning image inpainting Journal of Innovative Optical Health Sciences
2024, 17(3): 2350026
1 西京学院 材料与能源科学技术研究院,陕西 西安 710123
2 北京星航机电装备有限公司,北京 100074
3 西北工业大学 光电与智能研究院,陕西 西安 710072
在计算机视觉领域中,基于孪生网络的跟踪算法相比于传统算法提高了精度和速度,但是仍会受到目标遮挡、变形、环境变化等影响,导致孪生网络的跟踪算法的性能降低。为了深入了解基于孪生网络的单目标跟踪算法,本文对现有基于孪生网络目标跟踪算法进行了总结和分析,主要包括在孪生网络中引入注意力机制方法、超参数推理方法和模板更新方法,对这3种方法的目标跟踪算法进行了综述,详细介绍了国内外近几年基于孪生网络的算法研究和发展现状。对3个方面的代表算法采用VOT2016、VOT2017、VOT2018和OTB-2015数据集进行实验对比,获得了多种基于孪生网络的目标跟踪算法的性能。最后对基于孪生网络的目标跟踪算法进行了总结,并对未来的发展方向进行了展望。
计算机视觉 目标跟踪 孪生网络 深度学习 computer vision target tracking Siamese networks deep learning
1 中国科学院水利部成都山地灾害与环境研究所,成都 610299
2 中国科学院大学,北京 100049
冰湖溃决洪水是一种严重的山地自然灾害,威胁着中国高寒区的居民及铁路公路等重要基础设施,自动高效的冰湖遥感制图方法是冰湖灾害评估、监测预警的基础,然而现有自动制图方法在实际冰湖提取应用上难以达到传统人工和半自动冰湖提取方法上的精度,仍需进一步提高。文章在原生U-Net模型基础上,在各桥连接部分融合极化自注意力机制,将输入影像特征分别在空间和通道层保持高分辨率,并通过非线性合成输出细腻的特征,构建了一种改进的U-Net冰湖遥感深度学习制图方法,并将其成功应用在高原铁路关键区。研究结果表明:1)与PSPNet、DeepLabV3+、原生U-Net三种经典模型相比,改进模型在冰湖预测数据集上的各项指标上都有提升,精确率、召回率、交并比和F1值分别达到了0.972 5、0.966 5、0.940 8和0.969 4,相较于原生U-Net网络,精确度、召回率、交并比和F1值分别提高了5.01%、6.05%、10.73%和5.53%;2)基于Landsat-8卫星遥感数据,应用改进模型完成了2013—2022年帕隆藏布和易贡藏布案例区冰湖信息自动高效提取,如2020年冰湖总体精度为98.16%,与参照数据的重叠度达到96.66%,提取的精度满足冰湖灾害评估和监测预警研究需求,可用于铁路等重大工程沿线冰湖灾害防治的实践。
遥感监测 冰湖灾害 深度学习 自注意力机制 U-Net卷积神经网络 remote sensing monitoring glacial lake disaster deep learning self-attention mechanism U-Net
1 黔西南州自然资源管理服务中心,兴义 562400
2 贵州大学矿业学院,贵阳 550025
针对传统喀斯特地区裸岩提取方法成本高、精度低的问题,文章构建了一种基于改进DeepLabV3+的裸岩提取方法。该方法首先在编码器中用CA-DC-MobileNetV3替换DeepLabV3+骨干网络Xception进行特征提取,很大程度上减少了模型的参数量;其次,将编码器提取的特征通过特征金字塔网络和坐标注意力机制进行加强特征提取,以获取更多小目标信息并减少图像细节损失;最后在空洞空间金字塔池化模块将不同空洞率的卷积层进行特征融合,提高信息的利用率。研究结果表明:文章方法在不同场景裸岩提取任务中表现最好,模型参数量约为DeepLabV3+的1/13,交并比、F1分数分别为72.46%、84.03%,上述2个指标相比于DeepLabV3+模型分别提高了4.62和3.19个百分点,并优于其余常用语义分割模型,提高了裸岩提取精度。
裸岩提取 深度学习 语义分割 坐标注意力机制 bare rock extraction deep learning semantic segmentation coordinate attention mechanism
强激光与粒子束
2024, 36(4): 043019
强激光与粒子束
2024, 36(4): 043016
南京航空航天大学 电子信息工程学院,江苏南京211106
太阳能电池片(Photovoltaic, PV)表面缺陷检测是光伏组件生产中不可或缺的流程。基于机器视觉的自动缺陷检测方法因其高精度、实时性、低成本等优点得到了广泛应用。本文综述了基于机器视觉的太阳能电池片表面缺陷检测方法的研究进展。首先,阐述了太阳能电池片表面成像方式,列举了典型缺陷类型。然后重点分析了基于传统机器视觉算法及基于深度学习算法进行太阳能电池片表面缺陷检测的原理。将传统机器视觉算法分为图像域分析法、变换域分析法进行综述;从无监督学习、有监督学习和弱监督及半监督学习三个方面分别概述了近几年来基于深度学习的太阳能电池片表面缺陷检测的研究现状。对太阳能电池片表面缺陷检测各种典型方法进一步细分归类和对比分析,总结了每种方法的优缺点。随后,介绍了9种太阳能电池片表面缺陷图像数据集及缺陷检测性能评价指标。最后,系统总结了太阳能电池片缺陷检测常见的关键问题及其解决方法,对太阳能电池片表面缺陷检测的未来发展趋势进行了展望。
太阳能电池 缺陷检测 机器视觉 深度学习 检测网络 solar cells defect detection machine vision deep learning detection network