Two novel missense mutations in HAIRY-AND-ENHANCER-OF-SPLIT-7 in a family with spondylocostal dysostosis

Spondylocostal dysostosis (SCD) is an inherited disorder with abnormal vertebral segmentation that results in extensive hemivertebrae, truncal shortening and abnormally aligned ribs. It arises during embryonic development by a disruption of formation of somites (the precursor tissue of the vertebrae, ribs and associated tendons and muscles). Four genes causing a subset of autosomal recessive forms of this disease have been identified: DLL3 (SCDO1: MIM 277300), MESP2 (SCDO2: MIM 608681), LFNG (SCDO3: MIM609813) and HES7 (SCDO4). These genes are all essential components of the Notch signalling pathway, which has multiple roles in development and disease. Previously, only a single SCD-causative missense mutation was described in HES7. In this study, we have identified two new missense mutations in the HES7 gene in a single family, with only individuals carrying both mutant alleles being affected by SCD. In vitro functional analysis revealed that one of the mutant HES7 proteins was unable to repress gene expression by DNA binding or protein heterodimerization.     

Telematics-based online client–server/client collaborative environment for radiotherapy planning simulations

Customized cancer radiation treatment planning for each patient is very useful for both a patient and a doctor because it provides the ability to deliver higher doses to a more accurately defined tumor and at the same time lower doses to organs at risk and normal tissues. This can be realized by building an accurate planning simulation system to provide better treatment strategies based on each patient’s tomographic data such as CT, MRI, PET, or SPECT. In this study, we develop a real-time online client–server/client collaborative environment between the client (health care professionals or hospitals) and the server/client under a secure network using telematics (the integrated use of telecommunications and medical informatics). The implementation is based on a point-to-point communication scheme between client and server/client following the WYSIWIS (what you see is what I see) paradigm. After uploading the patient tomographic data, the client is able to collaborate with the server/client for treatment planning. Consequently, the level of health care services can be improved, specifically for small radiotherapy clinics in rural/remote-country areas that do not possess much experience or equipment such as a treatment planning simulator. The telematics service of the system can also be used to provide continued medical education in radiotherapy. Moreover, the system is easy to use. A client can use the system if s/he is familiar with the Windows^TM operating system because it is designed and built based on a user-friendly concept. This system does not require the client to continue hardware and software maintenance and updates. These are performed automatically by the server.     

基于配准方法的颅脑CT图像病变信息自动提取算法

医学图像中病变信息的计算机自动提取是实现计算机智能辅助诊断的关键与难点,本研究的目的就是提出一个解决该难题的算法,称之为PATHOINFER。该算法的基本过程是首先选择一幅具有代表性的模板图像帆和一系列与其相应的正常图像样奉Mi,利用非刚性配准分别建立表示“正常图像”灰度变化的灰度均值图谱,表示正常变异的统计概率图谱和反映其解剖结构空间关系的分割模板。以实现对“正常图像”的计算机描述。再通过M0与目标图像S的配准,达到“正常图像”与S在空间关系上的一致,然后通过S与“正常图像”的比较,利用模糊逻辑推理,自动检出S中的病变区域,并实现对其病变特征信息的自动提取。实验结果表明,PATHOINFER算法可自动地检出并分割病变区域,并能够自动地提取包括病变发生部位在内的特征信息。实现了计算机智能辅助诊断研究中病变信息自动提取的难胚。     

基于Rician分布的颈动脉内中膜厚度的测定

目的 颈动脉内中膜厚度是定量评价心血管疾病的核心指标之一。本文提出一种半自动基于超声影像的测量颈动脉内中膜厚度的计算机辅助方法,并在分割算法中引入了符合超声影像的Rician分布。方法 原始Chan-Vese模型较为适用于颈动脉血管壁的边界线的提取,但是原始模型假设图像斑点噪声符合分段常值分布,这对于本文所使用的颈动脉超声图像是不准确的。本文在原始的Chan-Vese模型上进行了相应的改进,提出超声斑点噪声符合Rician分布的Chan-Vese分割模型,然后通过二次分割提取内中膜边界,最后进行内中膜厚度测量。结果 将该方法用于实际的超声颈动脉图像,结果较为理想。结论 本方法能快速准确地提取颈动脉内中膜,并无须对原始图像做预处理。     

Quantitative Image Analysis in Mammary Gland Biology

In this paper we present a summary of recent quantitative approaches used for the analysis of macro and microscopic images in mammary gland biology. The advantages and disadvantages of whole mount analysis, reconstruction of serial tissue sections and nucleus/cell segmentation of either conventional and confocal images are discussed, as are applications of quantitative image analysis, such as quantification of protein levels or vasculature measurements in normal tissue and cancer. Integration of quantitative imaging into the further study of the mammary gland holds the promise of better understanding its tissue complexity that evolves during development, differentiation and disease.     

Automatic brain segmentation in preterm infants with post‐hemorrhagic hydrocephalus using 3D Bayesian U‐Net

Post‐hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two‐dimensional measurements of the ventricles. Automatic and reliable three‐dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U‐Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U‐Net, and ensemble learning using DenseNets and U‐Nets. A total of 41 T₂‐weighted MRIs from 27 preterm infants were manually segmented into lateral ventricles, external CSF, white and cortical gray matter, brainstem, and cerebellum. These segmentations were used as ground truth for model evaluation. All methods were trained and evaluated using 4‐fold cross‐validation and segmentation endpoints, with additional uncertainty endpoints for the Bayesian method. In the lateral ventricles, segmentation endpoint values for the DenseNet, U‐Net, ensemble learning, and Bayesian U‐Net methods were mean Dice score = 0.814 ± 0.213, 0.944 ± 0.041, 0.942 ± 0.042, and 0.948 ± 0.034 respectively. Uncertainty endpoint values for the Bayesian U‐Net were mean recall = 0.953 ± 0.037, mean  negative predictive value = 0.998 ± 0.005, mean accuracy = 0.906 ± 0.032, and mean AUC = 0.949 ± 0.031. To conclude, the Bayesian U‐Net showed the best segmentation results across all methods and provided accurate uncertainty maps. This method may be used in clinical practice for automatic brain segmentation of preterm infants with PHH, and lead to better PHH monitoring and more informed treatment decisions.     

Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data

RATIONALE AND OBJECTIVES: Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models. MATERIALS AND METHODS: We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. RESULTS: The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization. CONCLUSIONS: The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.     

市场细分技术在武汉市无偿献血者招募中的应用

目的了解目标献血者的构成和轮廓,以提高血站运作效率,并使目标献血者最大化地响应招募。方法借鉴社会营销中的市场细分技术,选择无偿献血知信行(KAP)、生活方式和人口统计变量作为细分变量,使用聚类分析的对武汉市居民行细分分析:向18—55周岁的武汉市居民及无偿献血者发放问卷,并对所收回的1101份有效问卷作市场(群体)细分。结果分析得到4个细分群体,依据集群中心距离判断各个细分群体的最重要特征,依次为“自我意识型”、“健康关注型”、“积极合作型”和“疏远淡漠型”。结论4个细分群体在生活方式、献血动机与行为上有明显差异;血站对各个细分群体的无偿献血宣传和营销活动应根据不同的营销目标而采取相宜的招募策略。     

基于CUDA的快速三维医学图像分割

目的:三维分割是医学图像分析和可视化中的重要组成部分,也是医学图像分割中的一个难点。水平集方法在三维医学图像分割中有很广阔的应用前景,但是该算法的计算量大,不能达到实时处理的要求。针对这个问题,提出了一种基于CUDA的并行加速方法。方法:采用NVIDIA公司的GPGPU模型CUDA,利用图像像素的独立性和偏微分方程求解的并发性,提高C-V水平集算法的分割速度。给出了并行计算的流程图,并对C-V水平集算法在CUDA上的实现进行了详细介绍。结果:实现了C-V水平集并行加速算法,该方法在保证分割效果的前提下,具有更快的分割速度。结论:所提出的方法是切实可行的,实现了快速的三维医学图像分割。     

基于贪婪算法的Snake模型

目的：为改善经典活动轮廓模型的缺陷。方法：本文提出一种新的基于贪婪算法的活动轮廓模型,对其内部能量中加入轮廓平均长度项的控制,外部能量中加入梯度方向势能,并提出区域能量在贪婪算法中的快速求解方法。另外采用动态调整蛇点的算法,使蛇点数目能够自适应地变化。结果：通过与传统的GVF算法分割结果比较,本文的分割效果较理想。结论：说明该方法分割准确性高并且对于用户的初始轮廓选择要求不高,具有一定的实用价值。