启发式牙颌CT影像自动分割

准确快速地分割CT切片特征轮廓是医学图像三维重建的重要环节。现有的轮廓分割方法必须通过手动层层交互操作,不仅耗时而且分割精度不高。针对这种局限性,提出一种基于启发式牙颌CT影像自动分割方法。首先用拉普拉斯算子对CT图像序列进行边缘增强,其次用轮廓匹配映射技术实现轮廓启发式传递,最后基于收缩包围算法自动分割牙颌序列。以14例完整牙(每例28~32颗牙数据样本)锥束CT断层扫描图像序列进行实验,在相同条件下分别用所提出的轮廓自动提取方法和其他提取方法,对实验样本进行轮廓提取,得到单颗牙轮廓提取的平均用时和提取轮廓与真实轮廓之间的距离差平均值。实验结果显示,轮廓自动分割算法提取单颗牙轮廓的用时约为其他手工分割法提取单颗牙轮廓用时的23%,同时提取的轮廓质量和用传统方法提取的轮廓质量相当。该方法为CT数据特征区自动化分割提供一种可行且高效的方法,为进一步改进现有的CT影像分割和三维重建算法提供了新的思路。     

基于样条曲线的CT牙列图像齿间轮廓重构的研究

为了重构出相邻齿断面由于密度高度一致,造成在CT图像中丢失的边界轮廓线,先在已提取的牙列整体连续外轮廓上,根据切矢方向的变化,确定出相邻齿廓线的交汇点;再分别以两个相邻交汇点为端点,其间的轮廓点为型值点,构建标准B样条曲线作为部分齿廓线;然后,分别求出相邻齿廓线在交汇点处的一阶导矢,进而求出该交汇点处的平均导矢;最后,以两两相对的交汇点为端点,构建Hermit样条插值曲线,该曲线即作为相邻齿间被丢失的轮廓线。用该方法可以成功地对两颗牙齿进行边界区分,进而提取轮廓,以达到对单颗牙齿进行三维重建,在仿真上能满足临床要求,为后续的牙颌仿真工作奠定了基础。     

基于样条曲线的CT牙列图像齿间轮廓重构的研究

为了重构出相邻齿断面由于密度高度一致,造成在CT图像中丢失的边界轮廓线,先在已提取的牙列整体连续外轮廓上,根据切矢方向的变化,确定出相邻齿廓线的交汇点;再分别以两个相邻交汇点为端点,其间的轮廓点为型值点,构建标准B样条曲线作为部分齿廓线;然后,分别求出相邻齿廓线在交汇点处的一阶导矢,进而求出该交汇点处的平均导矢;最后,以两两相对的交汇点为端点,构建Hermit样条插值曲线,该曲线即作为相邻齿间被丢失的轮廓线.用该方法可以成功地对两颗牙齿进行边界区分,进而提取轮廓,以达到对单颗牙齿进行三维重建,在仿真上能满足临床要求,为后续的牙颌仿真工作奠定了基础.     

基于自适应标记分水岭算法的肝脏CT图像自动分割

目的为减少人工交互提出了基于自适应标记分水岭的CT系列图像肝脏区域自动分割算法。方法首先对图像进行形态学重构运算以平滑图像,然后计算多尺度形态学梯度,同时提出利用梯度图像非零的局部极小值点的均值进行自适应标记提取,以避免分水岭的过分割和欠分割,再结合肝脏为最大的实质性脏器和相邻图像的相似性实现CT系列图像的肝区自动分割。结果该算法能自动、快速地提取CT系列图像中的肝脏区域。结论分水岭算法能准确定位区域的边缘,通过选择合适的阈值对梯度图像进行标记以抑制分水岭的过分割,实现医学图像中感兴趣区域的自动分割。     

基于区域自适应形变模型的CT图像牙齿结构测量方法研究

针对三维计算机断层扫描(CT)体数据的牙齿分割问题,本文提出了一种基于区域自适应形变模型的CT图像牙齿结构测量方法。本文方法结合了自动阈值分割、CV活动轮廓模型和图割方法,利用自动阈值分割实现牙冠的分割与定位,然后利用牙冠分割结果作为初始轮廓逐层分割牙齿。在分割难度最大的牙根上采用CV活动轮廓和图割互补的方法实现了牙根的准确分割。实验结果表明本文提出的牙齿结构测量方法能够准确地自动分割出牙齿的牙冠部分,进而在牙冠分割基础上快速准确地分割出牙颈和牙根。本文提出的牙齿结构测量方法能够准确地从临床CT牙齿数据中分割提取牙齿结构,鲁棒性强、精度高,可以有效辅助医生的临床治疗。     

基于贝叶斯决策的髋关节自动分割方法北大核心

背景:基于CT图像的髋关节分割技术已广泛应用于计算机辅助手术规划、假体设计和有限元分析。目的:探讨基于贝叶斯决策的髋关节自动分割方法在计算机辅助髋关节手术中的应用效果。方法:针对髋关节序列CT图像中骨骼近端分割精度低,计算复杂度高,自动化程度低等问题,提出了一种自动分割算法,通过对比度增强、阈值分割和区域增长等算法提取股骨的初步轮廓,再根据贝叶斯决策论对股骨边缘进行再次分割。结果与结论:基于贝叶斯决策的髋关节自动分割方法计算速度快,鲁棒性高,分割准确,在计算机辅助髋关节手术及假体设计等方面具有一定的实用价值。     

三维几何建模与仿真技术在牙颌修复中的应用

提出正常人牙颌组织从CT数据处理到目标组织几何建模及仿真设计的技术路线.将CT断层图像经过线性插值构造出三维数据场,分割出目标牙颌组织后,利用Marching Cubes算法提取牙颌组织的三角面几何模型.采用层切法重建完整的牙列咬和面,其层切精度达到0.2mm.经过配准,实现层切法和CT扫描所得图形线框的坐标拟合.然后在PowerSHAPE中对牙列单元进行三维重建,形成一套易于修改、方便组合的模型库;最后通过编程建立了牙列缺损与修复的几何仿真系统.     

基于改进区域生长算法的肝脏分割方法研究

目的：把肝脏从医学图像中提取出来,为肝脏三维定位以及放疗计划制定提供准确的数据。肝脏与其周围器官组织灰度差别小、边界不明显,而传统区域生长算法生长准则单一,不能满足分割精确度需求,并且未经处理的轮廓比较粗糙。针对这些问题,本文提出一种改进的区域生长算法。方法：本文算法主要从三个方面改进：基于先验经验和肝脏特性的种子区域选择;基于Canny算子边缘检测结果的区域生长准则动态优化;基于漫水填充法和曲线拟合的轮廓后处理。结果：本文使用多套临床实际腹部CT序列测试算法,以医生手动勾画结果为标准进行评价。在大多数CT切片上的肝脏自动分割都能取得较好的结果,并且分割用时很短,保证了效率。结论：测试结果表明,本文算法在动态控制区域生长和平滑轮廓方面有很好的作用,在保证速度的同时有效提高了肝脏自动分割精度。     

基于主动轮廓的三维气管树自动分割方法

在计算机辅助手术领域,对CT图像中肺部区域的气管和支气管进行准确分割和提取具有重要意义。气管和支气管的分割提取有助于手术的导航参考,准确的提取结果有助于避免手术中对支气管的损伤。本文设计了一种全自动的三维肺气管分割算法:首先,将基于主动轮廓的GCS方法引入到肺气管分割当中,在三维图像中得到粗分割结果;然后,利用灰度重建的方法将粗分割结果中分离的部分变得连通;最后利用三维区域生长提取支气管树。实验结果表明,本文方法能够稳定地分割出不同病例的支气管树,与自动阈值区域生长分割结果相比较,本文结果在支气管分叉数上有很大的提升,最少提升有28%,最多达86%。     

基于BP神经网络的CT图像骨皮质分割

目的在CT图像中通过对骨皮质的分割与测量,测定骨量、骨骼的几何形状以及骨强度,并计算相应的组织形态计量学参数。方法通过DCMTK解读CT图像,提取相应的图像信息。利用OpenCV对图像进行预处理,在感兴趣的区域(ROI)设置的基础上,提取图像的纹理特征作为输入向量;以对训练样本手工分割的结果作为导师信号,对BP神经网络进行训练;用训练好的网络对CT图像序列中的骨皮质进行分割,并对分割后的结果进行后处理及显示。结果骨皮质CT图像的神经网络分割效率符合实际应用的需求,分割结果形状明显,与周围组织区分度高,满足临床诊断需求。结论纹理特征明显的情况下,可以达到较为满意的分割效果。分割结果轮廓平滑,分割精度高、成功率高、适应性强;而且图像分割过程人工介入少,可以用于整套CT图像骨皮质的批量分割。不足之处在于此方法神经网络训练时间相对较长。     

基于改进离散曲线演化模型的牙齿三维特征点提取方法研究

在牙种植技术中,牙齿特征点的有效提取对后续的三维配准和重建具有重要的意义,现有方法的计算效率比较低;论文改进了离散曲线演化算法,采用曲线特征因子量描述牙齿CT各断层图像边缘曲线的复杂性,并根据曲线特征因子量自适应确定不同层间图像边缘曲线特征点提取的数目,以降低数据存储的冗余量,提高特征点的提取效率;用改进的离散曲线演化算法对牙齿不同层的临床CT图像提取特征点,并将实验结果与现有离散曲线演化算法的结果进行比较。结果表明,改进方法在提取每层CT图像特征点所需时间约为原算法的50%,同时提取的特征点数约为原算法的80%。将改进方法提取的特征点按不同比率进行三次样条曲线插值并进行后期重建,其重建效果能很好地反映牙齿的真实结构。因此,改进方法的计算效率远高于离散曲线演化算法,在牙齿种植领域中具有临床应用前景。     

Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images

We propose a new curvature-based method for correcting the segmented lung boundary. Our method consists of the following steps. First, the lungs are extracted from chest CT images by the automatic segmentation method. Second, the segmented lung contours are corrected by lung smoothing in each axial slice. Our scan line search provides an efficient contour tracing and curvature calculation. Finally, the smoothed lung contours are corrected by 3D VOI refinement. This increases the smoothness in the z-axis without distortion of the lung boundary. Experimental results show that our method effectively incorporates the pleural nodules and pulmonary vessels into the segmentation results.     

Automated cerebrum segmentation from three-dimensional sagittal brain MR images

We present a fully automated cerebrum segmentation algorithm for full three-dimensional sagittal brain MR images. First, cerebrum segmentation from a midsagittal brain MR image is performed utilizing landmarks, anatomical information, and a connectivity-based threshold segmentation algorithm as previously reported. Recognizing that cerebrum in laterally adjacent slices tends to have similar size and shape, we use the cerebrum segmentation result from the midsagittal brain MR image as a mask to guide cerebrum segmentation in adjacent lateral slices in an iterative fashion. This masking operation yields a masked image (preliminary cerebrum segmentation) for the next lateral slice, which may truncate brain region(s). Truncated regions are restored by first finding end points of their boundaries, by comparing the mask image and masked image boundaries, and then applying a connectivity-based algorithm. The resulting final extracted cerebrum image for this slice is then used as a mask for the next lateral slice. The algorithm yielded satisfactory fully automated cerebrum segmentations in three-dimensional sagittal brain MR images, and had performance superior to conventional edge detection algorithms for segmentation of cerebrum from 3D sagittal brain MR images.     

Shape-based grey-level image interpolation.

The three-dimensional (3D) object data obtained from a CT scanner usually have unequal sampling frequencies in the x-, y- and z-directions. Generally, the 3D data are first interpolated between slices to obtain isotropic resolution, reconstructed, then operated on using object extraction and display algorithms. The traditional grey-level interpolation introduces a layer of intermediate substance and is not suitable for objects that are very different from the opposite background. The shape-based interpolation method transfers a pixel location to a parameter related to the object shape and the interpolation is performed on that parameter. This process is able to achieve a better interpolation but its application is limited to binary images only. In this paper, we present an improved shape-based interpolation method for grey-level images. The new method uses a polygon to approximate the object shape and performs the interpolation using polygon vertices as references. The binary images representing the shape of the object were first generated via image segmentation on the source images. The target object binary image was then created using regular shape-based interpolation. The polygon enclosing the object for each slice can be generated from the shape of that slice. We determined the relative location in the source slices of each pixel inside the target polygon using the vertices of a polygon as the reference. The target slice grey-level was interpolated from the corresponding source image pixels. The image quality of this interpolation method is better and the mean squared difference is smaller than with traditional grey-level interpolation.     

基于改进Mask R-CNN的牙齿识别与分割

针对当前的研究方法在牙齿全景X光片上提取的信息较为单一,而未曾考虑将牙齿的类别信息与形状位置信息融合提取的问题,提出一种实例分割方法同时实现牙齿识别与分割。主要通过融合跳跃结构和SE（Squeeze and Excitation）模块对Mask R-CNN实例分割模型中的分割分支进行改进,并以牙齿功能与FDI牙位两种类别编码方式,采用400张牙齿全景X光片数据进行实验仿真。实验结果表明改进后的模型相比于其他模型,可以同时有效地进行牙齿分类和分割,实现牙齿类别、形状、位置信息的融合提取,改善了Mask R-CNN实例分割模型在分割分支中语义信息提取不足的问题。     

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1?cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation CBCT sampled on the segmentation contour candidate. The cost function measured the goodness of fit of the segmentation on the validation image and was minimized using a simplex optimizer. For each validation CBCT image, the segmentations were done five times using a different reference CBCT. The one with the lowest cost function was selected as the final bladder segmentation. Volume- and distance-based metrics and the accuracy of plan selection were evaluated to quantify the performance. Two to four PCA modes were needed to represent the bladder shape variation with less than 0.1?cm average residual error for the training data of each patient. The automatically segmented bladders had a 78.5% mean conformity index with the manual delineations. The mean SD of the local residual error over all patients was 0.24?cm. The agreement of plan selection between automatic and manual bladder segmentations was 77.5%. PCA is an efficient method to describe patient-specific bladder deformation. The statistical-shape-based segmentation approach is robust to handle the relatively poor CBCT image quality and allows for fast and reliable automatic segmentation of the bladder on CBCT for selecting the appropriate plan from a library of plans.     

Automatic Delineation of the Diaphragm in Computed Tomographic Images

Segmentation of the internal organs in medical images is a difficult task. By incorporating a priori information regarding specific organs of interest, results of segmentation may be improved. Landmarking (i.e., identifying stable structures to aid in gaining more knowledge concerning contiguous structures) is a promising segmentation method. Specifically, segmentation of the diaphragm may help in limiting the scope of segmentation methods to the abdominal cavity; the diaphragm may also serve as a stable landmark for identifying internal organs, such as the liver, the spleen, and the heart. A method to delineate the diaphragm is proposed in the present work. The method is based upon segmentation of the lungs, identification of the lower surface of the lungs as an initial representation of the diaphragm, and the application of least-squares modeling and deformable contour models to obtain the final segmentation of the diaphragm. The proposed procedure was applied to nine X-ray computed tomographic (CT) exams of four pediatric patients with neuroblastoma. The results were evaluated against the boundaries of the diaphragm as identified independently by a radiologist. Good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm over a total of 73 CT slices including the diaphragm.     

基于2.5D级联卷积神经网络的CT图像胰腺分割方法

目的:由于胰腺体积小、形态个体差异性大，影像上的准确分割较为困难。本文提出一种基于2.5D级联卷积神经网络的CT图像胰腺分割方法。方法:实验中使用的数据为NIH胰腺分割公开数据集，共包含82例腹部CT图像，随机选取其中56、9、17例分别作为训练集、验证集和测试集；训练过程中使用旋转、拉伸、平移、裁剪等操作对数据进行扩增。实验中提出一种用于胰腺分割的、结合概率图的2.5D级联深度监督UNet，即CSNet（Cascading deep Supervision UNet）。该网络由3个部分组成:第1部分基于UNet，输入连续5层图像，输出中间3层对应的粗分割图像，设置适当的阈值，使其变成二值的粗分割结果；第2部分将第1层、第3层的粗分割结果与中间层的原始图像相结合，输入另一个深度监督UNet网络，得到中间层的精细分割；第3部分将第1部分网络输出的中间层的粗分割概率图与第2部分网络输出的细分割概率图通过1×1卷积进行概率融合得到最终的输出结果。3个子网络同时进行训练，对应的能量函数联合优化，从而得到更精准的分割结果。最后，使用DSC对分割结果进行评估。结果:在独立测试集上，CSNet实现了（83.74±5.27）%的DSC值。结论:CSNet可以准确分割出CT图像上的胰腺区域。