基于直方图拟合的MRI图像水平集分割

利用混合高斯模型对MRI图像直方图进行分析,将拟合获得的特征参数作为水平集曲线进化的约束条件,对医学图像进行分割。分割中采用的自适应Level Set方法,能够自适应地确定曲线进化方向（扩张或收缩）,而不必在分割之前指定其进化方向,减少了人工干预;同时也克服了传统测地活动轮廓线（GAC）方法对图像梯度信息的过分依赖,以及由于对图像进行大尺度高斯平滑处理造成边缘点移动、定位准确度下降的缺陷。分别对MRI仿真和真实图像进行了实验,MRI仿真实验的分割敏感性、专一性和总体性能指标分别达到了94.72%、97.52%和97.22%。分割结果的定量分析和定性分析表明算法的有效性以及较高的分割准确度。     

基于形态学的均值平移算法的神经元干细胞图像的自动分割

在神经元干细胞的图像分析中,准确快速的图像分割是干细胞分化增值自动追踪系统的基础。为了准确地分割低对比度的灰度神经元干细胞图像,本研究提出一种基于形态学运算和均值平移算法的神经元干细胞分割方法,称其为形态学的均值平移算法。此算法可以快速地获得任意形状细胞的图像,并且能检测到图像中多连接边缘不封闭的细胞。将此方法应用于神经元干细胞序列图像分割中并且将其与门限分割、水线分割和活动轮廓进行对比。实验结果证明,与其它的方法相比,此方法获得的细胞分割形状更接近于真实细胞的形状,并且能获得或接近于原始图像中准确的独立细胞数目。此方法可以获得正确的分割结果,为进一步图像处理奠定了良好的基础。     

催化剂分割算法的初步研究

目的：在TS—MRF模型基础上，针对基于二叉树分割算法的分割停止判断提出新的算法。方法：在分割图像中引入试探图像作为“催化剂”，并以“催化剂”在分割结果中是否显现为依据来判断二叉树分割的候选结点是否应该停止分割，具体分割算法采用MRF分割。结果：对多组模拟图像进行了初步分割实验，实验表明该方法可以对实验图像的树状分割过程给出正确的停止判断。结论：催化剂分割算法可有效地对待分割图像进行正确的类数判断。     

Micromelic dwarfism with cone epiphyses,metaphyseal dysplasia,and vertebral segmentation defects

We present the clinical and radiological findings in a newborn male with severe micromelic dwarfism, short neck, short and narrow upper thorax, and brachydactyly. At the age of 1 year mental development is slightly retarded. The X-ray findings of severe vertebral segmentation defects and a generalized metaphyseal skeletal dysplasia did not lead to a final, conclusive diagnosis. The present patient may be the first example of a new type of micromelic spondyloepi-metaphyseal dysplasias. © 1996 Wiley-Liss, Inc.     

Fetal ultrasound image segmentation system and its use in fetal weight estimation

A semi-automated fetal ultrasound image segmentation system is developed to improve the estimation of fetal weight (EFW). Four standardized fetal parameters are measured by the proposed segmentation system: biparietal diameter, head circumference, abdominal circumference and femur length. Computerized measurements of 215 fetuses are compared with manual measurements in term of fitness analysis and difference analysis. Among 215 cases, computerized measurements of 103 fetuses within 3 days of delivery are utilized in the fetal weight estimation. The EFW based on computerized measurements and manual measurements are compared by using regression analysis, artificial neural network and support vector regression. By using different estimation methods, the computerized measurements decrease the EFW errors about 40–70 g. The lowest mean absolute percentage error of EFW decrease from 6.71% for manual measurements to 4.66% for computerized measurements. The proposed fetal ultrasound image segmentation system can provide more accurate EFW in antepartum examination.     

部分缺失牙齿咬合面自适应重建算法

针对数字化口腔修复技术能够高效、可靠地实现对缺损牙体精确修复的特点,提出了一种部分缺失牙齿咬合面自适应重建算法,通过变形对应牙位上的标准牙模型得到重建后的缺失咬合面。首先基于缺损牙体的洞缘线信息将其划分为内、外两个标识区域;其次设计分块策略将标准牙曲面划分为3个变形区域;最后对标准牙施加微分变形操作,将与缺失区域相对应的标准牙曲面作为重建后的咬合面。对3类常见的嵌体预备牙型的修复体设计试验表明,重建后的咬合面均能够保持局部细节形态,与预备体缺失区域匹配良好,为口腔修复CAD/CAM系统的临床应用提供了一套完整的嵌体修复体设计方案。     

基于改进C-V模型的肿瘤CT图像分割

针对传统C-V模型演化速度慢和不能很好分割灰度不均匀图像的缺点,从两个方面进行了改进。首先采用一个新颖的基于局部梯度的模型,使C-V模型初始轮廓曲线快速移到目标边界附近,大大缩短了演化时间;其次,结合GVF模型从两个方向指向目标边界的特点,为C-V模型的速度方程添加一个自适应速度调节项,使模型收敛于真实边界。通过肝脏肿瘤CT图像的分割,验证该方法是有效的。     

Evaluation of Computer-Assisted Quantification of Carotid Artery Stenosis

The purpose of this study was to evaluate the influence of advanced software assistance on the assessment of carotid artery stenosis; particularly, the inter-observer variability of readers with different level of experience is to be investigated. Forty patients with suspected carotid artery stenosis received head and neck dual-energy CT angiography as part of their pre-interventional workup. Four blinded readers with different levels of experience performed standard imaging interpretation. At least 1 day later, they performed quantification using an advanced vessel analysis software including automatic dual-energy bone and hard plaque removal, automatic and semiautomatic vessel segmentation, as well as creation of curved planar reformation. Results were evaluated for the reproducibility of stenosis quantification of different readers by calculating the kappa and correlation values. Consensus reading of the two most experienced readers was used as the standard of reference. For standard imaging interpretation, experienced readers reached very good (k = 0.85) and good (k = 0.78) inter-observer variability. Inexperienced readers achieved moderate (k = 0.6) and fair (k = 0.24) results. Sensitivity values 80%, 91%, 83%, 77% and specificity values 100%, 84%, 82%, 53% were achieved for significant area stenosis >70%. For grading using advanced vessel analysis software, all readers achieved good inter-observer variability (k = 0.77, 0.72, 0.71, and 0.77). Specificity values of 97%, 95%, 95%, 93% and sensitivity values of 84%, 78%, 86%, 92% were achieved. In conclusion, when supported by advanced vessel analysis software, experienced readers are able to achieve good reproducibility. Even inexperienced readers are able to achieve good results in the assessment of carotid artery stenosis when using advanced vessel analysis software.     

基于卷积神经网络和Transformer的肝脏CT图像分割方法

针对现有的卷积神经网络在肝脏图像分割上精度较低的问题,提出了一种以U-Net网络模型为基础的分割算法。将多头自注意力机制引入到U-Net网络的跳跃连接中,在编码器部分使用空洞卷积,采用混合损失函数从而提高分割精度。在LITS数据集上通过实验结果表明,利用本文方法进行肝脏分割与传统U-Net方法相比Dice系数提升3.3%,平均交并比提升了2.4%,平均像素准确率提升了3.66%。     

基于多尺度特征融合与反向注意力的COVID-19病灶分割

针对新型冠状病毒肺炎（COVID-19）分割问题中感染区域具有高变异性以及病灶与背景对比度低等问题,提出一种基于多尺度特征融合与反向注意力的COVID-19感染分割网络。首先,利用残差网络作为主干网络进行特征提取,并使用全局上下文聚合策略对不同层次特征进行融合得到粗略的分割结果;其次,在网络瓶颈处添加多尺度特征融合模块,利用空洞卷积与多核池化增强网络分割不同尺度病变的能力;最后,设计一种级联结构的反向注意力模块,利用互补区域的细节特征增强背景与目标的对比度。本文方法在COVID-19 CT分割测试集上的准确率、特异性、灵敏度分别达到0.714、0.700和0.958,误检和漏检区域明显减少,细小病灶的分割能力显著提升。