目的:探索基于大津法的自衰减校正PET(sacPET)重建技术在 18F-florbetapir(AV45)显像中的应用。 方法:2018年11月到2019年12月间在中山大学附属第一医院前瞻性招募7例确诊的阿尔茨海默病(AD)患者[男4例，女3例，年龄(69.6±4.5)岁]和3名健康对照者(HC)[男1名，女2名，年龄(68.0±4.6)岁]行 18F-AV45 PET显像，利用PET采集的原始数据进行sacPET重建。通过视觉分析和半定量分析技术将sacPET数据与标准PET数据进行对比。采用Fisher确切概率法、 Kappa检验和Pearson相关分析数据。 结果:HC组和AD组中，sacPET图像和标准PET图像的放射性分布大致相似，HC者sacPET图像的灰质、白质对比度弱于标准PET图像，而AD患者的sacPET图像上皮质高摄取区域范围小于标准PET图像。视觉分析显示在所有的受试者中，sacPET图像示阳性区域19个，标准PET示阳性区域22个，各脑区的阳性率在2种PET图像间差异无统计学意义(均 P>0.05)，各个脑区显像结果的总体一致性为88.00%[44/50； Kappa＝0.75(95% CI: 0.57～0.94), P<0.05]。半定量分析显示，sacPET图像所得到的额叶与扣带回标准摄取值比值(SUVR)低于标准PET图像(0.93±0.06与0.96±0.06和0.99±0.04与1.01±0.04； t值：5.30和5.10,均 P<0.01)，而顶叶、颞叶和枕叶所得SUVR值高于标准PET图像(0.78±0.08与0.68±0.07、0.97±0.07与0.91±0.08和0.94±0.11与0.71±0.12； t值：6.27、7.36和16.90，均 P<0.01)；2种PET图像大脑皮质SUVR相关性有统计学意义( r＝0.75, P<0.001)。 结论:在 18F-AV45显像中，sacPET重建技术无需CT扫描即可得到可靠有效的PET数据，但其准确性和精度仍有待提高。

From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Com-puted Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu's variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detect-ing fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

目的 提出一种新颖的核素肾动态显像肾脏感兴趣区自动勾画算法.方法 选取30位行核素肾动态显像患者作为研究对象,首先对原始肾图做形态学运算、强度对增强和高斯滤波等预处理,去除非感兴趣区和提升图像对比度;接着采用最大类间差法(Otsu)自适应确定最佳阈值,完成肾脏的初步分割;然后形态学操作和边界追踪被用来提取肾脏边界.结果 基于本文算法的分割结果与专家手工分割结果具有很高的相关性,平均真符合率达91%,平均假误符合率为13.4%,平均假符合率为9.3%,边界误差距离为1.6个像素.且基于本文算法的Dice相似性系数(0.9061±0.0196)和平均耗时(2.1477±0.2835)s均优于其他算法.结论 基于本文的分割算法能快速准确的提取肾脏感兴趣区域,可应用于核素肾动态显像肾小球滤过率的测定.

Ectopic adiposity has gained considerable attention because of its tight association with metabolic and cardiovascular health in persons with spinal cord injury (SCI). Ectopic adiposity is characterized by the storage of adipose tissue in non-subcutaneous sites. Magnetic resonance imaging (MRI) has proven to be an effective tool in quantifying ectopic adiposity and provides the opportunity to measure different adipose depots including intermuscular adipose tissue (IMAT) and intramuscular adipose tissue (IntraMAT) or in-tramuscular fat (IMF). It is highly important to distinguish and clearly define these compartments, because controversy still exists on how to accurately quantify these adipose depots. Investigators have relied on separating muscle from fat pixels based on their characteristic signal intensities. A common technique is plotting a threshold histogram that clearly separates between muscle and fat peaks. The cut-offs to separate between muscle and fat peaks are still not clearly defined and different cut-offs have been identified. This review will outline and compare the Midpoint and Otsu techniques, two methods used to determine the threshold between muscle and fat pixels on T1 weighted MRI. The process of water/fat segmentation using the Dixon method will also be outlined. We are hopeful that this review will trigger more research towards accurately quantifying ectopic adiposity due to its high relevance to cardiometabolic health after SCI.

目的 为了获取独立的内耳三维模型,探讨一种半自动、快速的内耳分割方法.方法 将MRI图像导入3D slicer软件,基于Otsu法,采用半自动分割内耳,并结合meshlab软件进行内耳后期精细处理.结果 对30例MRI影像数据进行分割,一侧内耳完成半自动分割仅需3钟左右,内耳三维模型图像清晰,内耳信息保留完整.结论 Otsu法进行内耳半自动分割操作简单、快捷,内耳模型结构完整,三维成像效果好.

The following mini-review attempts to guide researchers in the quantification of fluorescenfly-labelled proteins within cultured thick or chromogenically-stained proteins within thin sections of brain tissue.It follows from our examination of the utility of Fiji ImageJ thresholding and binarization algorithms.Describing how we identified the maximum intensity projection as the best of six tested for two dimensional (2D)-rendering of three-dimensional (3D) images derived from a series of z-stacked micrographs,the review summarises our comparison of 16 global and 9 local algorithms for their ability to accurately quantify the expression of astrocytic glial fibrillary acidic protein (GFAP),microglial ionized calcium binding adapter molecule 1 (IBA1) and oligodendrocyte lineage Olig2 within fixed cultured rat hippocampal brain slices.The application of these algorithms to chromogenically-stained GFAP and IBAI within thin tissue sections,is also described.Fiji's BioVoxxel plugin allowed categorisation of algorithms according to their sensitivity,specificity accuracy and relative quality.The Percentile algorithm was deemed best for quantifying levels of GFAP,the Li algorithm was best when quantifying IBA expression,while the Otsu algorithm was optimum for Olig2 staining,albeit with over-quantification of oligodendrocyte number when compared to a stereological approach.Also,GFAP and IBA expression in 3,3'-diaminobenzidine (DAB)/ haematoxylin-stained cerebellar tissue was best quantified with Default,Isodata and Moments algorithms.The workflow presented in Figure 1 could help to improve the quality of research outcomes that are based on the quantification of protein with brain tissue.

投影旋转中心(COR)精准定位是确保计算机断层成像(CT)重建图像质量的关键要素,经典的互相关匹配算法在投影角度为 0～180° 时难以满足高质量 CT 成像要求,需进行改进创新.本文根据正弦图上 0° 与180° 投影数据翻转后有对应性的特点提出基于这两行数据平移匹配的 COR 校正算法,该算法利用 OTSU 进行阈值分割以减少背景噪声影响,通过 L1 范数量化 COR 最小偏移得到准确校正值后进行 CT 重建.分别采用加入随机梯度噪声和高斯噪声的 Sheep-Logan 模型和雄性 SD 大鼠样本的同质肝脏与异质牙齿图像验证新算法的有效性,并将新算法与互相关匹配算法做性能对比.结果表明:新算法运算量少、简便快速且具有良好的抗噪鲁棒性,校正精度高(稀疏采样投影数据在 10％～50％ 时也能很好地校正 COR 值),CT 重建图像质量有显著改进,效果优于互相关算法.

目的:基于配准开源平台ITK和开源计算机显示视觉库OpenCV开发刚性配准程序,并集成到DeepPlan计划系统中,实现快速准确的多模态刚性配准.方法:基于形态学开运算初步去除图像中无需关注的细小区域和部分扫描床,使用最大类间方差法(Otsu)突出感兴趣的图像部位,Canny算子用于提取富含信息区域的边界信息.使用像素填充技术得到图像配准需要的掩膜,并采用OpenMP并行技术加速掩膜计算过程.最终在配准过程中将掩膜作用于参考图像或浮动图像.结果:测试了多组不同模态和部位的算例,实验结果表明基于掩膜优化的多模态医学图像刚性配准方法可以自动去除绝大部分背景图像和扫描床板,节约图像配准中一半以上时间,且图像配准质量并无下降;在1 min内可以完成两组100张左右的图像配准.且本方法以动态链接库的形式成功集成在治疗计划系统DeepPlan中.结论:在保证配准结果准确的基础上,基于掩膜优化的多模态医学图像刚性配准方法显著提高了图像配准速度,且算法稳定性能高,有很好的临床应用前景.

目的 探讨建立双侧内耳统计形状模型的方法和影响因素,并探讨使用平均双侧内耳模型作为带空间信息的标准模型.方法 通过Otsu法以及手动分割37例双侧内耳模型,对模型进行配准并生成统计形状模型,观察测量标准模型空间信息.结果 Otsu法可以分割获取高质量内耳模型,模型配准后使用Statismo 0.81软件可以生成稳定的内耳统计形状模型,其导出的平均模型空间方向信息具有代表性.结论 双侧内耳统计形状模型可以导出平均模型作为带空间方向信息的标准模型.