基于改进凸包算法的肺实质分割研究

肺实质的精确分割一直都是肺部疾病计算机辅助诊断的重要研究内容,传统的分割方法大多只能分割出不包含病灶的肺实质区域,为后期的图像分析与辅助决策带来很大的影响。针对具有边缘型肺结节的肺部CT图像,提出一种实现简单且实验效果较好的肺实质分割算法。首先,利用常规方法提取肺实质的粗略轮廓;然后,针对上一步骤中肺实质病灶信息等的缺失现象,提出一种改进的二维凸包算法对肺实质的外轮廓进行再修复;最后,利用区域生长和形态学运算,修复肺实质的内部轮廓。运用新算法,对200张边缘型肺结节的肺部CT图像进肺实质分割。实验结果表明：与已有的“滚球法”和凸包算法修复肺实质相比,新算法具有较高的准确率,可以达到90%以上,边缘型肺结节等病灶信息能被较为准确地表示出来,为建立高效的肺部疾病诊断系统奠定基础。     

运用肺气肿自动定量软件CIP评估COPD患者 肺叶功能的研究

目的 探讨运用CIP软件自动分割肺叶、定量分析单个肺叶及全肺肺气肿指数在评估慢性阻塞性肺疾病患者肺叶功能中的价值。方法 选取2018年7月~12月在我院就诊和住院的稳定期COPD患者23例,行胸部CT薄层扫描,运行CIP软件对其肺叶进行自动分割,定量测定各肺叶及全肺肺气肿指数。结果 CIP软件能将肺叶进行自动分割及计算肺气肿指数,直观了解病变分布的位置,能得到各个肺叶的损伤数据,23例COPD患者中肺气肿1级18例,2级5例。结论 CIP软件可以较好的消除人为因素的影响,快速准确的定量计算肺气肿指数,明确责任肺叶组织的损伤程度,为评估肺储备功能及外科肺减容手术提供参考依据。     

右肺叶切除术后新叶间裂的X线表现

对32例行右肺叶切除术前后的正侧位胸片的叶间裂及新叶间裂进行了系统观察,探讨右肺部分新叶切除术后叶间裂的表现,借以判断余肺膨胀度及提高对肺叶重分布的认识。结果全部病例新叶间裂表现以侧位片为主,右上肺切除后新叶间裂相似于左斜裂,右中肺切除后新叶裂表现为从后上方至前下方较水平的细线影,右下叶肺切除后新叶间裂呈两型,Ⅰ型为类似左斜裂形态,但上缘起点较低。Ⅱ型为从中部向下方倾斜的细线影。分析在右侧位胸片上新叶间裂的形成和表现,对肺叶重分布作出客观的认识。     

肺叶间裂变异的64层MSCT多平面重组图研究

目的为影像学诊断提供肺叶间裂的变异资料。方法运用64层螺旋进行CT扫描,选择451例肺部无明显病变或手术影响观察肺叶间裂的患者,应用MPR技术显示、观察并统计肺叶间裂及其变异情况。结果451例被检查者中有肺叶间裂变异者占69.18%(312例);其中,右肺变异者占62.53%(282例),左肺变异者占18.18%(82例)。右肺282例中177例存在一种变异,105例合并其它变异。左肺82例中25例存在一种变异,57例均合并其它的变异。肺叶间裂变异存在3种类型:(1)肺叶间裂不全,其中右水平裂不全占45.45%(205例),右斜裂不全占19.51%(88例),左斜裂不全占16.41%(74例);(2)肺叶间裂缺如:其中右水平裂缺如占0.89%(4例),右水平裂合并斜裂缺如占0.22%(1例),左斜裂缺如占0.44%(2例)(;3)附属裂:存在附属裂的占13.97%(63例),其中右肺附属裂占11.97%(54例),左肺附属裂占2.44%(11例)。结论64层螺旋CT扫描结合多平面重组技术能显示肺叶间裂各种变异,可为临床学科提供可靠、有价值的影像学资料。     

左肺3叶1例

在解剖教学过程中,发现右肺、左肺各分为3叶的个体1例。该个例为成年男性,因意外事故死亡,经尸体解剖打开胸腔见其双肺为浅灰褐色,右肺混有较多黑色斑点,左肺有少量散在黑色斑点。表面光滑、有光泽,质地及弹性良好。双肺分叶基本对称,右肺由水平裂和斜裂分为3叶,与常人无异。左肺较窄而长,心切亦明显,有水平裂和斜裂将其分为3叶(见附图),斜裂与常人左肺叶间裂相近,起自肺门,向后上在肺尖下绕过后缘,向前下达肺底,至肺门前下方;水平裂(左肺副裂)较短,约自腋中线处起自斜裂,向前绕过前缘,至肺门前上方。国人肺叶分叶一般为左2右3。有介绍左肺…     

肺结节检测算法研究

探讨基于CT图像数据的肺结节自动检测算法.肺结节提取一般步骤为:CT图像预处理、肺实质分割、肺结节提取.     

肺门及肺裂的解剖与临床

目的:为临床肺移植及肺叶切除术提供解剖学依据。方法:采用成人尸体40具(80个肺),术中对60个肺(左40,右20)进行了解剖学观测。结果:支气管动脉在肺根处行于左支气管的上、下缘及右支气管的后壁和下缘,并紧贴支气管壁,在肺根处外径为1.70±0.5mm。80个左肺有完全性肺裂者64例(80％);60个右肺有完全性斜裂者49例(81.7％),有完全性水平裂者47例(78.3％)。肺叶间连结根据连结组织量的多少,可分为4型,以Ⅰ型为主(占50％)。结论:支气管动脉与胸廓内动脉管径相近,且在肺根处恒定易于游离,二者吻合较为理想。肺叶切除术中注意迷走支气管及血管的出现。     

基于肺部CT图像中肺实质分割的研究进展

肺部CT图像中肺实质的精确分割是肺部疾病诊断和治疗的一个重要步骤,也是制约计算机辅助检测技术广泛应用于肺部疾病诊断领域的主要瓶颈之一。基于近年来肺部CT图像中肺实质分割的研究进展,对其分割过程中的5个步骤进行综述,包括预处理、初分割、精分割、左右肺分离、边缘修补。最后展望了肺部CT图像中肺实质分割的发展方向。     

基于边界逼近的肺实质分割方法

肺部CT图像的分割是计算机辅助诊断系统处理的一个重要环节,其分割的结果影响到医生的诊断与进一步的分析。由于胸膜结节的灰度与肺实质外围的灰度相近,运用传统的分割方法无法正确分割出此类病灶。将胸膜结节包含肺实质一起分割出来,使计算机辅助诊断系统能够对此类病灶做进一步的分析。提出一种结合Graham算法以及边界逼近的方法,对肺实质的轮廓进行修正,进而得到修正的二值模板;将该模板与原图像做乘运算,得到包含胸膜结节的肺实质。运用所提出的方法,对公开数据库LIDC中68张含病灶的CT样本图像做处理,通过与传统方法的对比以及对算法的过分割比例、欠分割比例以及准确性的分析,得到准确率为98.5%,平均过分割比例为1.35%,平均欠分割比例为0.51%,证明了该方法的有效性。     

基于低剂量CT图像的肺实质分割方法

为了提高早期肺癌筛查中肺结节计算机辅助检测、辅助诊断的准确性,提出一种多种方法结合的低剂量CT图像肺实质自动分割算法。首先利用改进的多方向形态学滤波算法进行预处理;然后利用聚类法、flood-fill算法去除背景,实现粗分割;接着利用引入霍夫变换的改进三维区域生长算法去除气管和主支气管树;最后利用分水岭算法和二维凸包算法实现肺实质细分割。实验结果通过对ELCAP数据库中的50个低剂量CT序列利用本研究算法进行处理,验证了该算法的有效性,正确分割率达到95.75%。为肺结节检测等后期的诊断提供了有价值的参考信息。     

右肺斜裂、水平裂的CT应用解剖

目的为临床CT显示右肺斜裂下部的影像。为描述肺裂的CT影像特征提供解剖学资料。方法在成人右肺标本45例中,取20例在肺门的4个层面上作常规CT扫描,观察其中12例的CT图像特征,并与标本相对照。分离45例右肺门和肺裂,在4个平面上观察肺裂的出现率和叶间面的形态等。结果斜裂在肺门上部平面出现率为17.8%,在下叶底段支气管下部平面的出现率为97.8%、CT显示率为83.3%,斜裂叶间面在第1、3、4层面(包括水平裂)均为带状影,在第2层面为凸向后的弧形带状影;斜裂与肺水平轴间的下夹角为57.7°±7.9°。结论对右肺下叶底段支气管下部层面作CT扫描可以显示斜裂下部的影像;将诊断床向足侧下倾32.3°或将CT架上倾32.3°可作CT斜位扫描,有助于对两种肺裂的影像进行鉴别。     

A Fully Automatic Method for Lung Parenchyma Segmentation and Repairing

Considering that the traditional lung segmentation algorithms are not adaptive for the situations that most of the juxtapleural nodules, which are excluded as fat, and lung are not segmented perfectly. In this paper, several methods are comprehensively utilized including optimal iterative threshold, three-dimensional connectivity labeling, three-dimensional region growing for the initial segmentation of the lung parenchyma, based on improved chain code, and Bresenham algorithms to repair the lung parenchyma. The paper thus proposes a fully automatic method for lung parenchyma segmentation and repairing. Ninety-seven lung nodule thoracic computed tomography scans and 25 juxtapleural nodule scans are used to test the proposed method and compare with the most-cited rolling-ball method. Experimental results show that the algorithm can segment lung parenchyma region automatically and accurately. The sensitivity of juxtapleural nodule inclusion is 100 %, the segmentation accuracy of juxtapleural nodule regions is 98.6 %, segmentation accuracy of lung parenchyma is more than 95.2 %, and the average segmentation time is 0.67 s/frame. The algorithm can achieve good results for lung parenchyma segmentation and repairing in various cases that nodules/tumors adhere to lung wall.     

The azygos lobe: an unusual anatomical observation with pathological and surgical implications

The azygos lobe is a rare anomaly in broncho-pulmonary segmentation due to an unusual course of the azygos vein. Its radiological aspects are well known but there are few anatomical reports about its bronchial and vascular components. The authors describe the characteristic features in a particular case of the azygos lobe observed in the right lung after studying a fresh specimen and doing a casting of said specimen. This azygos lobe was in a position medial to the right upper lobe and above the hilum. It had the shape of an egg and was 5 cm high, 4 cm wide and 2 cm thick. The azygos fissure was of a vertical form. The lobe was ventilated by the posterior branch of the apical segmental bronchus (B1a). This latter was accompanied by two apical sub-segmental arteries (A1ai, A1aii) and the apical intersubsegmental vein (V1a). The new findings were that: first, the azygos lobe bronchus supplied a part of the right upper lobe; second, the passage of the azygos vein deformed the bronchus of the right upper lobe, and not that of the azygos lobe. This means that it was the right upper lobe, rather than the azygos lobe that was predisposed to the pathology. So, in excising this type of azygos lobe, particular precautions have to be taken to spare the bronchus of the azygos lobe that supplies the right upper lobe.     

Automatic Segmentation of Pulmonary Fissures in Computed Tomography Images Using 3D Surface Features

Pulmonary interlobar fissures are important anatomic structures in human lungs and are useful in locating and classifying lung abnormalities. Automatic segmentation of fissures is a difficult task because of their low contrast and large variability. We developed a fully automatic training-free approach for fissure segmentation based on the local bending degree (LBD) and the maximum bending index (MBI). The LBD is determined by the angle between the eigenvectors of two Hessian matrices for a pair of adjacent voxels. It is used to construct a constraint to extract the candidate surfaces in three-dimensional (3D) space. The MBI is a measure to discriminate cylindrical surfaces from planar surfaces in 3D space. Our approach for segmenting fissures consists of five steps, including lung segmentation, plane-like structure enhancement, surface extraction with LBD, initial fissure identification with MBI, and fissure extension based on local plane fitting. When applying our approach to 15 chest computed tomography (CT) scans, the mean values of the positive predictive value, the sensitivity, the root–mean square (RMS) distance, and the maximal RMS are 91 %, 88 %, 1.01 ± 0.99 mm, and 11.56 mm, respectively, which suggests that our algorithm can efficiently segment fissures in chest CT scans.     

Automatic segmentation of lung fields in chest radiographs

The delineation of important structures in chest radiographs is an essential preprocessing step in order to automatically analyze these images, e.g., for tuberculosis screening support or in computer assisted diagnosis. We present algorithms for the automatic segmentation of lung fields in chest radiographs. We compare several segmentation techniques: a matching approach; pixel classifiers based on several combinations of features; a new rule-based scheme that detects lung contours using a general framework for the detection of oriented edges and ridges in images; and a hybrid scheme. Each approach is discussed and the performance of nine systems is compared with interobserver variability and results available from the literature. The best performance is obtained by the hybrid scheme that combines the rule-based segmentation algorithm with a pixel classification approach. The combinations of two complementary techniques leads to robust performance; the accuracy is above 94% for all 115 images in the test set. The average accuracy of the scheme is 0.969 +/- 0.0080, which is close to the interobserver variability of 0.984 +/- 0.0048. The methods are fast, and implemented on a standard PC platform.     

Toward an automated analysis system for nuclear magnetic resonance imaging. II. Initial segmentation algorithm

Image segmentation algorithms based on hierarchical clustering have been developed for analysis of T1 and T2 nuclear magnetic resonance images. Application of these algorithms to simultaneous T1-T2 images of healthy volunteers extracted fundamental tissue types in the brain. These algorithms also were used both to identify the extent of the region of involvement of a subject with a history of a grade 3 astrocytoma of the right frontal lobe of the brain, and to characterize the tissue within the region of involvement. These results suggest that a simple segmentation algorithm can produce reasonable clustering of tissue types within the brain.     

肝脏面的形态学观测

对52例成人尸肝进行脏面结构观测，检出肝圆韧带隧道14例（26．9％），腔静脉管3例（5．8％），肝门右侧额外裂40例（76．9％），尾状叶下纵沟37例（71．2％），此外，对肝尾状突的长径，肝圆韧带裂，静脉韧带等结构的宽度作了测量，所得数据对解剖结构的数据化及应用解剖学和影像解剖学有参考价值。     

A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computer-aided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations.     

国人肝段的再认识

目的:对肝内门静脉和肝静脉重新认识,提出一种新的国人肝段划分方法,为影像学和肝外科提供断层解剖学资料。方法:使用50例上腹部连续断层标本和20例多层螺旋CT图像及三维重建图像,研究了肝内门静脉的走行和分布以及肝静脉及其属支的回流范围及其两者之间的相互关系。结果:国人肝段新的划分方法:门静脉右支主干存在时,依肝中静脉所在的正中裂将肝分为左、右半肝。右半肝被一弯曲的右叶间裂分成右前上叶和右后下叶。右前上叶依垂直段间裂分为腹侧和背侧段。右前上叶的腹侧段被水平亚段间裂分为上、下两个亚段。右后下叶依水平段间裂分为上、下两段。肝左静脉主干存在时,依肝左静脉主干所在的左叶间裂将左半肝分成左后上叶和左前下叶。左前下叶依左段间裂分为内侧和外侧段。水平亚段间裂将左前下叶的内侧段分为上、下两个亚段。依弧形背裂分尾状叶和右前上叶及左前下叶内侧段。结论:国人肝段新的划分法不仅有利于肝内微小病变的精确定位,而且便于肝外科探索新的和更加安全的术式来施行各种肝切除和肝移植。     

Application of an inverse kernel concept to Monte Carlo based IMRT

Inverse treatment planning by means of pencil beam algorithms can lead to errors in the calculation of dose in areas without secondary electron equilibrium. Monte Carlo (MC) simulations give accurate results in such areas but result in increased computation times. We present a new, so-called inverse kernel concept that offers MC precision in inverse treatment planning with acceptable computation times and memory consumption. Inverse kernels are matrices that describe the dose contribution from all bixels of a beam to a distinct voxel of the patient phantom. The concept is similar to other generalized pencil-beam concepts, except that inverse kernel elements are precalculated using a single MC simulation and stored as binary trees. In this procedure a modified MC code (XVMC) is applied to trace the photon history for each dose deposition. Iterative optimization is then applied in a second step. The inverse process is separated into (i) a slower MC simulation and (ii) a faster iterative optimization, followed by (iii) the segmentation procedure, and (iv) a final MC dose calculation step including a segment weight reoptimization. Inverse kernel optimization, or IKO, with segmentation and reoptimization steps is demonstrated by means of a lung cancer case. To demonstrate the superiority of an inverse MC system over pencil-beam or collapsed-cone based systems, the final result of the IKO is compared to plans where all segments have been calculated by pencil beam or collapsed cone, respectively. Dose-volume histograms and dose-difference histograms show remarkable differences, which can be attributed to systematic errors in both algorithms. IKO is a precise, nonhybrid, inverse MC treatment planning system which suits current clinical needs, as several optimization steps can follow one single MC-simulation step for a distinct beam setup.