3D Segmentation of Medical Images Using a Fast Multistage Hybrid Algorithm

In this paper, we propose a fast multistage hybrid algorithm for 3D segmentation of medical images. We first employ a morphological recursive erosion operation to reduce the connectivity between the object to be segmented and its neighborhood; then the fast marching method is used to greatly accelerate the initial propagation of a surface front from the user defined seed structure to a surface close to the desired boundary; a morphological reconstruction method then operates on this surface to achieve an initial segmentation result; and finally morphological recursive dilation is employed to recover any structure lost in the first stage of the algorithm. This approach is tested on 60 CT or MRI images of the brain, heart and urinary system, to demonstrate the robustness of this technique across a variety of imaging modalities and organ systems. The algorithm is also validated against datasets for which “truth” is known. These measurements revealed that the algorithm achieved a mean “similarity index” of 0.966 across the three organ systems. The execution time for this algorithm, when run on a 550 MHz Dual PIII-based PC runningWindows NT, and extracting the cortex from brain MRIs, the cardiac surface from dynamic CT, and the kidneys from 3D CT, was 38, 46 and 23 s, respectively.     

二维与三维超声在眼科疾病中的对照研究

目的 旨在对比二维超声与三维超声在眼科疾病的应用价值。方法 利用高频探头对眼科不同类别的疾病进行二维图像与三维图像的对比观察。结果 三维图像能比二维图像有较高的空间立体成像感，尤其是在视网膜脱离与玻璃体积血中机化膜的鉴别诊断中有较高价值。结论 三维超声成像在大多数眼科疾病鉴别诊断中能提供较二维图像更多的信息。     

Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography

We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography’s challenges: high dimensional data, complex anatomical environments, and limited annotation data. First, we proposed a deep atlas network, which integrated LV atlas into the deep learning framework to address the 3D LV segmentation problem on echocardiography for the first time, and improved the performance based on limited annotation data. Second, we proposed a novel information consistency constraint to enhance the model’s performance from different levels simultaneously, and finally achieved effective optimization for 3D LV segmentation on complex anatomical environments. Finally, the proposed method was optimized in an end-to-end back propagation manner and it achieved high inference efficiency even with high dimensional data, which satisfies the efficiency requirement of clinical practice. The experiments proved that the proposed method achieved better segmentation results and a higher inference speed compared with state-of-the-art methods. The mean surface distance, mean hausdorff surface distance, and mean dice index were 1.52 mm, 5.6 mm and 0.97 respectively. What’s more, the method is efficient and its inference time is 0.02s. The experimental results proved that the proposed method has a potential clinical application for 3D LV segmentation on echocardiography.     

3-D Ultrasound Segmentation of the Placenta Using the Random Walker Algorithm: Reliability and Agreement

Volumetric segmentation of the placenta using 3-D ultrasound is currently performed clinically to investigate correlation between organ volume and fetal outcome or pathology. Previously, interpolative or semi-automatic contour-based methodologies were used to provide volumetric results. We describe the validation of an original random walker (RW)-based algorithm against manual segmentation and an existing semi-automated method, virtual organ computer-aided analysis (VOCAL), using initialization time, inter- and intra-observer variability of volumetric measurements and quantification accuracy (with respect to manual segmentation) as metrics of success. Both semi-automatic methods require initialization. Therefore, the first experiment compared initialization times. Initialization was timed by one observer using 20 subjects. This revealed significant differences (p < 0.001) in time taken to initialize the VOCAL method compared with the RW method. In the second experiment, 10 subjects were used to analyze intra-/inter-observer variability between two observers. Bland–Altman plots were used to analyze variability combined with intra- and inter-observer variability measured by intra-class correlation coefficients, which were reported for all three methods. Intra-class correlation coefficient values for intra-observer variability were higher for the RW method than for VOCAL, and both were similar to manual segmentation. Inter-observer variability was 0.94 (0.88, 0.97), 0.91 (0.81, 0.95) and 0.80 (0.61, 0.90) for manual, RW and VOCAL, respectively. Finally, a third observer with no prior ultrasound experience was introduced and volumetric differences from manual segmentation were reported. Dice similarity coefficients for observers 1, 2 and 3 were respectively 0.84 ± 0.12, 0.94 ± 0.08 and 0.84 ± 0.11, and the mean was 0.87 ± 0.13. The RW algorithm was found to provide results concordant with those for manual segmentation and to outperform VOCAL in aspects of observer reliability. The training of an additional untrained observer was investigated, and results revealed that with the appropriate initialization protocol, results for observers with varying levels of experience were concordant. We found that with appropriate training, the RW method can be used for fast, repeatable 3-D measurement of placental volume.     

User-Guided Segmentation of Preterm Neonate Ventricular System from 3-D Ultrasound Images Using Convex Optimization

A three-dimensional (3-D) ultrasound (US) system has been developed to monitor the intracranial ventricular system of preterm neonates with intraventricular hemorrhage (IVH) and the resultant dilation of the ventricles (ventriculomegaly). To measure ventricular volume from 3-D US images, a semi-automatic convex optimization-based approach is proposed for segmentation of the cerebral ventricular system in preterm neonates with IVH from 3-D US images. The proposed semi-automatic segmentation method makes use of the convex optimization technique supervised by user-initialized information. Experiments using 58 patient 3-D US images reveal that our proposed approach yielded a mean Dice similarity coefficient of 78.2% compared with the surfaces that were manually contoured, suggesting good agreement between these two segmentations. Additional metrics, the mean absolute distance of 0.65 mm and the maximum absolute distance of 3.2 mm, indicated small distance errors for a voxel spacing of 0.22 × 0.22 × 0.22 mm³. The Pearson correlation coefficient (r = 0.97, p < 0.001) indicated a significant correlation of algorithm-generated ventricular system volume (VSV) with the manually generated VSV. The calculated minimal detectable difference in ventricular volume change indicated that the proposed segmentation approach with 3-D US images is capable of detecting a VSV difference of 6.5 cm³ with 95% confidence, suggesting that this approach might be used for monitoring IVH patients' ventricular changes using 3-D US imaging. The mean segmentation times of the graphics processing unit (GPU)- and central processing unit-implemented algorithms were 50 ± 2 and 205 ± 5 s for one 3-D US image, respectively, in addition to 120 ± 10 s for initialization, less than the approximately 35 min required by manual segmentation. In addition, repeatability experiments indicated that the intra-observer variability ranges from 6.5% to 7.5%, and the inter-observer variability is 8.5% in terms of the coefficient of variation of the Dice similarity coefficient. The intra-class correlation coefficient for ventricular system volume measurements for each independent observer ranged from 0.988 to 0.996 and was 0.945 for three different observers. The coefficient of variation and intra-class correlation coefficient revealed that the intra- and inter-observer variability of the proposed approach introduced by the user initialization was small, indicating good reproducibility, independent of different users.     

Strain-Initialized Robust Bone Surface Detection in 3-D Ultrasound

Three-dimensional ultrasound has been increasingly considered as a safe radiation-free alternative to radiation-based fluoroscopic imaging for surgical guidance during computer-assisted orthopedic interventions, but because ultrasound images contain significant artifacts, it is challenging to automatically extract bone surfaces from these images. We propose an effective way to extract 3-D bone surfaces using a surface growing approach that is seeded from 2-D bone contours. The initial 2-D bone contours are estimated from a combination of ultrasound strain images and envelope power images. Novel features of the proposed method include: (i) improvement of a previously reported 2-D strain imaging-based bone segmentation method by incorporation of a depth-dependent cumulative power of the envelope into the elastographic data; (ii) incorporation of an echo decorrelation measure-based weight to fuse the strain and envelope maps; (iii) use of local statistics of the bone surface candidate points to detect the presence of any bone discontinuity; and (iv) an extension of our 2-D bone contour into a 3-D bone surface by use of an effective surface growing approach. Our new method produced average improvements in the mean absolute error of 18% and 23%, respectively, on 2-D and 3-D experimental phantom data, compared with those of two state-of-the-art bone segmentation methods. Validation on 2-D and 3-D clinical in vivo data also reveals, respectively, an average improvement in the mean absolute fitting error of 55% and an 18-fold improvement in the computation time.     

Fully Automatic Plaque Segmentation in 3-D Carotid Ultrasound Images

Automatic segmentation of the carotid plaques from ultrasound images has been shown to be an important task for monitoring progression and regression of carotid atherosclerosis. Considering the complex structure and heterogeneity of plaques, a fully automatic segmentation method based on media-adventitia and lumen-intima boundary priors is proposed. This method combines image intensity with structure information in both initialization and a level-set evolution process. Algorithm accuracy was examined on the common carotid artery part of 26 3-D carotid ultrasound images (34 plaques ranging in volume from 2.5 to 456 mm³) by comparing the results of our algorithm with manual segmentations of two experts. Evaluation results indicated that the algorithm yielded total plaque volume (TPV) differences of −5.3 ± 12.7 and −8.5 ± 13.8 mm³ and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm³. Moreover, high correlation coefficients in generating TPV (0.993 and 0.992) between algorithm results and both sets of manual results were obtained. The automatic method provides a reliable way to segment carotid plaque in 3-D ultrasound images and can be used in clinical practice to estimate plaque measurements for management of carotid atherosclerosis.     

Segmentation of Skin Tumors in High-Frequency 3-D Ultrasound Images

High-frequency 3-D ultrasound imaging is an informative tool for diagnosis, surgery planning and skin lesion examination. The purpose of this article was to describe a semi-automated segmentation tool providing easy access to the extent, shape and volume of a lesion. We propose an adaptive log-likelihood level-set segmentation procedure using non-parametric estimates of the intensity distribution. The algorithm has a single parameter to control the smoothness of the contour, and we describe how a fixed value yields satisfactory segmentation results with an average Dice coefficient of D = 0.76. The algorithm is implemented on a grid, which increases the speed by a factor of 100 compared with a standard pixelwise segmentation. We compare the method with parametric methods making the hypothesis of Rayleigh or Nakagami distributed signals, and illustrate that our method has greater robustness with similar computational speed. Benchmarks are made on realistic synthetic ultrasound images and a data set of nine clinical 3-D images acquired with a 50-MHz imaging system. The proposed algorithm is suitable for use in a clinical context as a post-processing tool.     

Automatic Segmentation of Calcifications in Intravascular Ultrasound Images Using Snakes and the Contourlet Transform

It is valuable to detect calcifications in intravascular ultrasound images for studies of coronary artery diseases. An image segmentation method based on snakes and the Contourlet transform is proposed to automatically and accurately detect calcifications. With the Contourlet transform, an original image is decomposed into low-pass bands and band-pass directional sub-bands. The 2-D Renyi's entropy is used to adaptively threshold the low-pass bands in a multiresolution hierarchy to determine regions-of-interest (ROIs). Then a mean intensity ratio, reflecting acoustic shadowing, is presented to classify calcifications from noncalcifications and obtain initial contours of calcifications. The anisotropic diffusion is used in bandpass directional sub-bands to suppress noise and preserve calcific edges. Finally, the contour deformation in the boundary vector field is used to obtain final contours of calcifications. The method was evaluated via 60 simulated images and 86 in vivo images. It outperformed a recently proposed method, the Santos Filho method, by 2.76% and 14.53%, in terms of the sensitivity and specificity of calcification detection, respectively. The area under the receiver operating characteristic curve increased by 0.041. The relative mean distance error, relative difference degree, relative arc difference, relative thickness difference and relative length difference were reduced by 5.73%, 19.79%, 11.62%, 12.06% and 20.51%, respectively. These results reveal that the proposed method can automatically and accurately detect calcifications and delineate their boundaries. (E-mail: yywang@fudan.edu.cn)     

Random Forest-Based Bone Segmentation in Ultrasound

Ultrasound (US) imaging is a safe alternative to radiography for guidance during minimally invasive orthopedic procedures. However, ultrasound is challenging to interpret because of the relatively low signal-to-noise ratio and its inherent speckle pattern that decreases image quality. Here we describe a method for automatic bone segmentation in 2-D ultrasound images using a patch-based random forest classifier and several ultrasound specific features, such as shadowing. We illustrate that existing shadow features are not robust to changes in US acquisition parameters, and propose a novel robust shadow feature. We evaluate the method on several US data sets and report that it favorably compares with existing techniques. We achieve a recall of 0.86 at a precision of 0.82 on a test set of 143 spinal US images.     

胆囊疾病的三维超声诊断

目的　评估三维超声在胆囊疾病中的临床应用价值。方法　超声诊断仪检查了 5 1例胆囊疾病的患者。首先用二维超声对胆囊病变进行采集 ,储存到计算机 ,处理成数据块。然后切换到三维超声 ,确定感兴趣区 ,进行三维重建分析。最后作三维的动态显示。结果　三维超声所提供的图像立体感强 ,形态逼真 ,定位准确。结论　三维超声能清晰地显示胆囊疾病的形态特点和内部结构 ,为该病提供了更为直观的诊疗信息     

经阴道三维超声对LUFS患者卵泡期卵巢血流灌注的评价

目的 利用经阴道三维超声定量分析LUFS患者卵泡期卵巢血流参数,探讨LUFS发生的卵泡期血流灌注特点.方法 选择在卵泡早期、卵泡中期、围排卵期行经阴道三维超声检查,应用VOCAL软件定量分析两组患者优势侧卵巢能量多普勒血流参数VI、FI、VFI.结果 对照组优势侧卵巢血流参数VI、FI、VFI在卵泡3个时期呈上升趋势(P＜0.01); LUFS组优势侧卵巢VI、VFI在各期逐渐升高(P＜0.05),FI则无明显周期性波动(P＞0.05);LUFS组与对照组比较,卵泡中期及围排卵期VI、FI、VFI明显降低(P＜0.05).对照组成熟卵泡血流分级以3～4级为主,LUFS组多呈1～2级.结论 成熟卵泡新生血管数量少,血流量低可能是LUFS发生的血流灌注特点.     

3D and 4D Ultrasound: Current Progress and Future Perspectives

Purpose of Review

Three-dimensional (3D) echocardiography (3DE) and 4-dimensional echocardiography (4DE), also known as real-time (RT) 3DE (RT3DE), are rapidly emerging technologies which have made significant impact in the clinical arena over the years. This review will discuss the recent applications of 3DE in diagnosing and treating different types of cardiovascular disease.

Recent Findings

Recent studies using 3DE expanded on prior findings and introduced additional applications to different cardiac conditions. Some studies have used 3D parameters to prognosticate long-term outcomes. Numerous innovative software designs including fully automated algorithms have been introduced to better evaluate valvular heart disease and cardiac function.

Summary

With further evolution of 3DE technologies, this imaging modality will emerge as a powerful tool and likely become the imaging modality of choice in the diagnosis and management of various cardiac disorders.

     

正常与隐性脊柱裂胎儿脊髓圆锥位置的三维超声评估

目的 探讨三维超声定位胎儿脊髓圆锥辅助诊断隐性脊柱裂的价值.方法 三维超声分别检测102例孕中晚期正常胎儿(正常组)及11例隐性脊柱裂胎儿(异常组)脊髓圆锥所对应椎体水平;检验三维超声法定位脊髓圆锥的组间、组内一致性;建立正常组脊髓圆锥位置与孕周的回归模型;比较两组脊髓圆锥位置的差异.结果 三维超声脊髓圆锥定位的操作者一致性检验加权Kappa值分别为1.0和0.788;正常组脊髓圆锥位置均位于L3或L3水平以上;异常组1例脊髓圆锥位置位于L3水平,10例低于L3水平.结论 三维超声可准确标记胎儿脊髓圆锥位置;隐性脊柱裂胎儿脊髓圆锥水平明显低于正常;脊髓圆锥定位是发现和诊断胎儿隐性脊柱裂的重要声像标志.     

三维矩阵探头实时双平面成像在超声引导介入诊疗中的应用

目的 探讨三维矩阵探头实时双平面成像在超声引导介入诊疗中的应用价值.方法 采用三维超声实时双平面成像模式(RT 3Dxplane)引导下对36例患者进行穿刺组织学活检及穿刺抽液介入治疗,同期对40例患者采用常规超声引导进行穿刺组织学活检.比较双平面成像与常规超声引导下,两组在穿刺时间、穿刺并发症、组织标本长度等方面有无差异.结果 全部双平面超声引导介入操作中,调节交互平面的不同部位,可确定针尖的位置及其与靶目标的相互关系.对于肾病肾穿刺患者,双平面引导组与对照组两者之间在穿刺时间及穿刺标本长度上均无统计学差异[分别为(440±10)s与(442±12)s,以及(1.0±0.20) cm与(0.9±0.26) cm],所有标本均完整并获得病理组织学诊断.对于穿刺并发症,双平面组少于对照组.结论 三维双平面成像技术引导超声介入操作可实时、清晰、直观地观察穿刺针与病变的空间位置关系,降低操作难度,增加操作医师的信心.     

3D Ultrasound Strain Imaging of Puborectalis Muscle

The female pelvic floor (PF) muscles provide support to the pelvic organs. During delivery, some of these muscles have to stretch up to three times their original length to allow passage of the baby, leading frequently to damage and consequently later-life PF dysfunction (PFD). Three-dimensional (3D) ultrasound (US) imaging can be used to image these muscles and to diagnose the damage by assessing quantitative, geometric and functional information of the muscles through strain imaging. In this study we developed 3D US strain imaging of the PF muscles and explored its application to the puborectalis muscle (PRM), which is one of the major PF muscles.     

Early Diagnosis of a Fetal Skeletal Dysplasia Associated with Increased Nuchal Translucency with 2D and 3D Ultrasound

We report a case of the prenatal diagnosis of a lethal skeletal dysplasia in a fetus presenting with increased nuchal translucency of 7.3 mm on a routine first trimester scan at 12 weeks of gestation. The karyotype was normal and there was no history of previous affected pregnancies. An abnormality of the fetal spine was first seen at 12 weeks of gestation, the malformation of the left hemithorax, where the processus transversi and the costal bones were replaced by a cystic structure, was detected at 13 weeks using a combination of two- and three-dimensional ultrasound. In order to identify correctly the underlying problem, at 16 weeks of gestation a detailed 2D and 3D ultrasound examination was performed. 3D surface images were calculated and a combination of surface reconstruction and maximum-mode for clear delineation of the fetal skeleton was used. In this way, the vertebral defects with absence of costal bones and processus transversi in the region of the left hemithorax, which were replaced by a cystic structure causing thoracic kyphoscoliosis and pulmonal hypoplasia and were associated with marked ventriculomegaly, could be clearly demonstrated. Although fetal autopsy was not carried out, the underlying abnormalities diagnosed by ultrasound suggest Jarcho-Levin syndrome as final diagnosis, which is characterized by multiple vertebral and rib malformations.