三维超声诊断胎儿宫内窘迫的临床价值

目的探讨三维（3D）超声检测胎儿宫内窘迫（IFD）的临床价值。方法用GElog9三维超声诊断仪对48例单胎孕妇分别进行二维多普勒超声检查（2D）和三维超声图像重建,通过观察胎心率,脐动脉收缩期峰值和舒张末期流速之比（S/D）,羊水,胎动等指标做出超声诊断。结果胎心率、胎动异常是判断胎儿有无缺氧和缺氧程度的主要指标;脐带、羊水异常是造成胎儿宫内窘迫的原因。重建的胎儿脐带血管解剖结构清晰,螺旋层次分明,脐带绕颈及周数一目了然,与2D超声相比较有较高的特异性和敏感性。结论 3D超声能直接形象和层次分明的显示脐带血管解剖形态、空间转向,诊断胎儿宫内窘迫的符合率100%,明显高于2D。为临床提供重要参考依据,对提高胎儿质量、预防和降低围产期胎儿死亡率具有重要的临床意义。     

Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure

Segmentation of a fetal head from three-dimensional (3-D) ultrasound images is a critical step in the quantitative measurement of fetal craniofacial structure. However, two main issues complicate segmentation, including fuzzy boundaries and large variations in pose and shape among different ultrasound images. In this article, we propose a new registration-based method for automatically segmenting the fetal head from 3-D ultrasound images. The proposed method first detects the eyes based on Gabor features to identify the pose of the fetus image. Then, a reference model, which is constructed from a fetal phantom and contains prior knowledge of head shape, is aligned to the image via feature-based registration. Finally, 3-D snake deformation is utilized to improve the boundary fitness between the model and image. Four clinically useful parameters including inter-orbital diameter (IOD), bilateral orbital diameter (BOD), occipital frontal diameter (OFD) and bilateral parietal diameter (BPD) are measured based on the results of the eye detection and head segmentation. Ultrasound volumes from 11 subjects were used for validation of the method accuracy. Experimental results showed that the proposed method was able to overcome the aforementioned difficulties and achieve good agreement between automatic and manual measurements.     

3D Freehand Ultrasound for in vivo Determination of Human Skeletal Muscle Volume

Skeletal muscle volume is an important indicator of muscle function. Three-dimensional (3D) freehand ultrasound provides a noninvasive method for determining muscle volume and is acquired using a standard clinical ultrasound machine and an external tracking system to monitor transducer position. Eleven healthy volunteers were scanned with a 3D freehand system that uses an optical tracking device. Interest was concentrated on one of the muscles of the quadriceps group, rectus femoris and volume measurements performed on 30 mm cross-sections were compared with measurements derived from magnetic resonance imaging. Measured muscle volumes ranged from 5 cm³ to 28 cm³. The mean difference between measurements from 3D freehand ultrasound and magnetic resonance was 0.53 cm³ with 95% limits of agreement of ±2.14 cm³. Muscle volume measurements obtained using 3D ultrasound were within ±16% of the corresponding value from magnetic resonance imaging. We have shown for the first time that 3D freehand ultrasound can be used to determine human skeletal muscle volume accurately in vivo. (E-mail: T.J.MacGillivray@ed.ac.uk)     

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks

Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasound images. Current approaches to automatic classification are limited to the use of either image patches (cropped images) or the global (whole) image. As many fetal organs have similar visual features, cropped images can misclassify certain structures such as the kidneys and abdomen. Also, the whole image does not encode sufficient local information about structures to identify different structures in different locations. Here we propose a method to automatically classify 14 different fetal structures in 2-D fetal ultrasound images by fusing information from both cropped regions of fetal structures and the whole image. Our method trains two feature extractors by fine-tuning pre-trained convolutional neural networks with the whole ultrasound fetal images and the discriminant regions of the fetal structures found in the whole image. The novelty of our method is in integrating the classification decisions made from the global and local features without relying on priors. In addition, our method can use the classification outcome to localize the fetal structures in the image. Our experiments on a data set of 4074 2-D ultrasound images (training: 3109, test: 965) achieved a mean accuracy of 97.05%, mean precision of 76.47% and mean recall of 75.41%. The Cohen κ of 0.72 revealed the highest agreement between the ground truth and the proposed method. The superiority of the proposed method over the other non-fusion-based methods is statistically significant (p < 0.05). We found that our method is capable of predicting images without ultrasound scanner overlays with a mean accuracy of 92%. The proposed method can be leveraged to retrospectively classify any ultrasound images in clinical research.     

时间-空间相关成像技术评价胎儿心脏形态结构的初步研究

目的评估3D／4D时间-空间相关成像（STIC）是否能比常规二维胎儿超声心动图提供更多胎儿心脏的切面及信息。方法采用3D／4DSTIC超声系统对26例孕龄16～41周的正常胎儿进行心脏和胸部的自动扫查，获得容积数据后进行脱机分析，主要对胎儿心脏的解剖结构进行多平面的观察和三维重建，重点观察胎儿心脏的流出道等结构。结果26例胎儿皆成功获得满意二维和三维四腔心切面的图像。通过STIC技术获得的容积数据显示了一系列相互垂直的三维平面图像（即A平面、B平面、C平面），对其中的每一幅图像都可通过旋转、平移进行控制、分析。切割或三维重建图像可获得比常规二维胎儿超声心动图检查更多的切面及信息。结论3D／4DSTIC技术比常规二维胎儿超声心动图检查能更快地提供更多的观察心脏解剖结构的切面和信息。     

50例胎儿颅后窝异常的超声诊断及预后分析

目的分析胎儿颅后窝异常超声诊断准确性及预后表现。 方法对2015年8月至2018年3月于首都医科大学附属北京妇产医院超声检查提示颅后窝异常50例胎儿的二维和三维超声图像进行分析,总结不同种类颅后窝异常胎儿产前超声声像图特点,与胎儿颅脑磁共振检查、出生后检查及随访结果对照,分析超声诊断胎儿颅后窝异常的准确性。 结果50例颅后窝异常胎儿经三维超声检查最终提示Dandy-Walker畸形8例,小脑蚓部发育不良8例,Blake′s陷窝囊肿6例,小脑发育不良5例,颅后窝蛛网膜囊肿9例,单纯小脑延髓池增宽13例,小脑蚓部形态异常1例。其中26例胎儿颅脑磁共振检查证实23例与产前三维超声提示诊断相符合,2例与产前三维超声提示诊断不符合,1例胎儿颅脑磁共振检查对超声诊断进行了补充。妊娠结局：50例胎儿经超声及颅脑磁共振检查后,24例孕妇选择终止妊娠（5例失访）;26例孕妇继续妊娠（小脑蚓部发育不良和小脑发育不良各1例,Blake′s陷窝囊肿4例,颅后窝蛛网膜囊肿7例,单纯性小脑延髓池增宽13例）。其中1例小脑蚓部发育不良胎儿出生后诊断为Joubert综合征,余25例胎儿预后良好。诊断准确性：与胎儿颅脑磁共振及出生后检查随访结果相比,产前二维超声诊断准确率为62.2%（28/45）,产前三维诊断准确率为88.9%（40/45）。 结论不同种类颅后窝异常（Dandy-Walker畸形、小脑蚓部发育不良、Blake′s陷窝囊肿）产前二维超声图像上表现相似,产前三维超声对颅后窝异常疾病分类及诊断有重要意义。     

胎儿骨骼系统发育异常的三维超声研究

目的：采用三维超声成像方法观察胎儿骨骼系统发育异常，了解其三维声像特征，探讨三维超声的辅助诊断价值。方法：对15例产前常规二维超声检查怀疑有胎儿骨骼系统发育异常的病例进行三维超声检查。三维成像方法采用表面成像和最大透明成像法，并对扫查存入的三维数据库同时进行多平面分析。通过与产后检查结果对照，比较二维和三维超声检查对诊断病变部位、形态改变细节的准确性。结果：15例胎儿骨骼系统发育异常中，无颅骨畸形2例；脊柱侧弯1例；脊椎裂1例；四肢短小畸形4例；上肢缺如2例；前臂畸形2例；手部缺指畸形2例；足内翻1例。所有病例都能够进行三维扫查并得出表面或最大透明三维立体图像。产前二维超声对病变部位和病变细节判断准确者分别是10例（66．7％）和8例（53．3％）；而三维超声对病变部位和病变细节判断准确者分别为15例（100％）和13例（86．7％）。结论：表面三维超声立体成像能够得出骨骼畸形的外貌特征，采用最大透明成像方式能够了解畸形的骨骼数量和形成改变。三维超声对胎儿骨骼系统发育异常的诊断提供了重要手段，是二维超声的重要补充。     

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     

Segmentation of embryonic and fetal 3D ultrasound images based on pixel intensity distributions and shape priors

This paper presents a novel variational segmentation framework combining shape priors and parametric intensity distribution modeling for extracting the fetal envelope on 3D obstetric ultrasound images. To overcome issues related to poor image quality and missing boundaries, we inject three types of information in the segmentation process: tissue-specific parametric modeling of pixel intensities, a shape prior for the fetal envelope and a shape model of the fetus’ back. The shape prior is encoded with Legendre moments and used to constraint the evolution of a level-set function. The back model is used to post-process the segmented fetal envelope. Results are presented on 3D ultrasound data and compared to a set of manual segmentations. The robustness of the algorithm is studied, and both visual and quantitative comparisons show satisfactory results obtained by the proposed method on the tested dataset.     

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.     

Assessment of the fetal nasal bone at 11-14 weeks of gestation by three-dimensional ultrasound.

OBJECTIVE: To evaluate the benefit of three-dimensional (3D) ultrasound in the examination of the fetal nasal bone at 11-14 weeks of gestation. METHOD: We examined the fetal nasal bone in 120 stored volumes acquired transabdominally with a 3D scanner from singleton pregnancies at 11-14 weeks of gestation. The volume acquisition had been performed following conventional ultrasound examination that had demonstrated presence of the fetal nasal bone. The volumes were obtained with two-dimensional (2D) start images in transverse, coronal mid-sagittal, parasagittal and oblique longitudinal sections of the fetal head. RESULTS: In the transverse and coronal sections, a satisfactory image demonstrating presence of the nasal bone was achieved in only three and one, respectively, of the 20 volumes that we obtained. In mid-sagittal sections, the nasal bone was always visible when the angle was within a range of 30-60 degrees, without the need for sectional image analysis. None of the images with an angle >60 degrees or <30 degrees was satisfactory. In the parasagittal sections with the fetal profile at 45 degrees, a good-quality image of the nasal bone was possible in all cases that were examined, irrespective of the distance from the mid-sagittal plane. In the oblique longitudinal sections with the fetal profile at 45 degrees, there were 10 volumes where the 2D start section was at 0-25 degrees from the midline and in all these cases the nasal bone was successfully visualized. In contrast, only 5/20 cases where the 2D start section was at 25-90 degrees from the midline provided a satisfactory image demonstrating the nasal bone. In 5/10 volumes obtained with the fetus facing downwards the nasal bone was visible in both the 2D and 3D images. CONCLUSION: In a 3D volume the extent to which the nasal bone can be demonstrated to be present in a given reconstructed section is entirely dependent on obtaining a good initial 2D view.     

eSTIC技术与STIC技术显示胎儿心脏诊断切面的对比研究

目的评价电子矩阵时间-空间相关成像（eSTIC）技术显示胎儿心脏诊断切面是否较时间-空间相关成像（STIC）技术具有优势。 方法2019年1月至6月在绍兴市妇女儿童医院超声科进行产前超声检查的孕妇中，随机选择四腔心观上脊柱在5点至7点的18~24周正常胎儿64例。分别应用电子矩阵容积探头、传统机械容积探头采集同一胎儿心脏容积数据，存盘并脱机分析。应用正交三平面模式、断层超声显像模式显示胎儿心脏8个诊断切面：标准四腔心观、左心室流出道观、右心室流出道观、三血管-气管观、腹部横切观、动脉导管弓长轴观、主动脉弓长轴观、上下腔静脉-右心房观。将各个切面观重建图像的质量分为优、一般、差三级，质量为优、一般定义为合格，质量差定义为不合格。应用配对χ²检验对比2种方法显示心脏各诊断切面的合格率，应用配对秩和检验对比2种方法获得的心脏各诊断切面的成像质量的差异。 结果2种技术均采集容积数据64个，采集成功率均为100%。eSTIC、STIC对于标准四腔心观、左心室流出道观、右心室流出道观、三血管-气管观、腹部横切观的显示合格率差异均无统计学意义（P均＞0.05），但对于动脉导管弓长轴观、主动脉弓长轴观、上下腔静脉-右心房观，eSTIC显示合格率均优于STIC（均为93.8% vs 71.9%），差异均具有统计学意义（χ²均=11.925，P均=0.001）。eSTIC所有诊断切面重建图像的质量均优于STIC，差异有统计学意义（P均＜0.05）。 结论eSTIC能显著提高胎儿心脏诊断切面重建图像的成像质量。与STIC相比，eSTIC对胎儿心脏纵向诊断切面的显示更具优势。     

三维超声对胎儿骨骼畸形的研究

目的 探讨三维超声在胎儿骨骼系统发育畸形成像中的临床价值。方法 采用表面及最大透明成像方法，对22例产前常规二维超声检查怀疑胎儿骨骼异常的病例，进行三维超声成像检查，并在诊断中运用电子刀技术，动态三维技术。通过和产后病理解剖对照，比较二维、三维图像提供畸形的部位及细节的准确性。结果 21例能进行三维扫查，并得出表面或最大透明图像，产前二维超声对病变部位及细节判断准确分别为16例（72.7%）,和9例(40.9%)，而三维超声判断准确分别为21例（95.5%）和20例(91%)。结论 表面三维超声立体成像能够得出骨骼畸形的外貌特征，采用最大透明成像方式，能够了解畸形的骨骼数量和形态改变。三维超声为胎儿骨骼系统发育异常的诊断，提供了重要手段，是二维超声的重要补充。诊断中，电子解剖刀，动态三维技术进一步提高了三维图像的准确性。     

Fetal lung volumetry using two- and three-dimensional ultrasound.

OBJECTIVES: To compare methods of measuring fetal pulmonary volume and to establish nomograms of fetal pulmonary volume according to gestational age for the accurate diagnosis of pulmonary hypoplasia. METHODS: Three methods of measuring fetal pulmonary volume in 39 normal fetuses were compared: two-dimensional (2D) ultrasound measurement assuming that the lung is a geometrical pyramid, three-dimensional (3D) ultrasound using the VOCAL rotational method, and the conventional multiplanar 3D mode. Linear regression was used to construct an equation for 3D volume calculation from 2D measurements (the re-evaluated pulmonary volume equation (RPVE)). Lung volume measurements were recorded from 622 singleton fetuses in order to construct nomograms. RESULTS: There was no statistically significant difference between the lung volume values obtained using the two 3D modes. However, in comparison with the 2D measurements the volumes obtained were larger (mean difference = 11.99, P < 0.1 x 10(-6)). The relationship between the 2D and 3D volumes was determined using a statistical linear regression method: RPVE (mL) = 4.24 + (1.53 x 2DGPV), where 2DGPV (2D geometric pulmonary volume) = (surface area right lung base (cm2) + surface area left lung base (cm2)) x 1/3 height right lung (cm). Two nomograms were constructed, one for use with 2D and one for 3D technology. CONCLUSION: 2D pulmonary volume assessment can be used in clinical situations where fetal prognosis depends on lung volume and its growth potential. It is routinely available and easy to perform particularly when repeat measurements are required in evaluation of lung growth. We therefore propose this method as an alternative to magnetic resonance imaging or 3D ultrasound.     

自动容积导航技术与二维超声获取胎儿颅脑正中矢状面的对比研究

目的评估三维超声自动容积导航技术（Smart Planes）与二维超声在获取胎儿颅脑正中矢状面（MSP）以及测量胎儿胼胝体及小脑蚓部参数的一致性和可重复性。 方法选取2017年10月至2017年12月在广东省妇幼保健院行产前超声检查的单胎妊娠孕妇168名作为研究对象。采用二维超声获取胎儿颅脑MSP并手动测量胼胝体前后径,测量小脑蚓部前后径、上下径及面积。随后应用三维容积探头获取胎儿颅脑容积数据,利用Smart Planes技术自动分析并测量胼胝体和小脑的相应数据。采用组内相关系数（ICC）及Bland-Altman法比较二维超声与Smart Planes技术对胎儿胼胝体以及小脑蚓部的测量的一致性和可重复性。对比不同操作者间测量的一致性及同一操作者采用两种方法测量的用时差异。 结果采用Smart Planes技术与二维超声获取胎儿颅脑MSP的成功率分别为97.0％（163/168）、79.8%（134/168）,差异有统计学意义（χ²=24.40,P<0.001）。其中76.8%（129/168）胎儿通过二维超声及Smart Planes技术均获得颅脑MSP并测量相关参数。比较二维超声与Smart Planes技术测量胼胝体前后径以及小脑蚓部前后径、上下径、面积的一致性,ICC分别为0.972、0.968、0.946、0.967。比较同一位操作者采用Smart Planes技术2次测量的一致性,ICC分别为0.995、0.987、0.962、0.993。比较不同操作者间采用Smart Planes技术测量的一致性,ICC分别为0.986、0.966、0.972、0.955。采用Smart Planes技术获取胎儿颅脑MSP所需时间[（13.49±5.38）s]明显短于采用二维超声所需时间[（89.99±113.42）s],差异有统计学意义（U=587.50,Z=-12.915,P<0.001）。 结论三维超声自动容积导航技术可快速获取胎儿颅脑MSP,测量所得胼胝体及小脑蚓部的参数与二维超声测量所得结果一致性较好,有助于超声医师对胎儿中线结构做出快速准确的识别和测量。     

二维及彩色多普勒超声诊断胎儿脐带扭转的临床分析

目的研究胎儿脐带扭转的二维声像图特征、彩色多普勒血流显像(CDFI)及脐动脉血流S/D值,分析其临床应用价值。方法对335例晚期妊娠妇女随机进行二维超声和CDFI检查。自脐带胎盘端到胎儿脐轮部循踪行多切面扫查,记录脐带扭转声像,脐动脉血流S/D测值,产后进行对照分析。结果CDFI诊断脐带扭转阳性检出率高,漏诊率低,显著优于二维超声。脐带扭转的声像图特征:纵切面呈绳索、麻花或双排对称串珠状;横、斜切面似“鼠眼样”形和“元宝”形。脐带扭转组胎儿窘迫、低Apgar评分发生率明显高于无扭转组(P<0.05)。结论脐带扭转与胎儿宫内窘迫、预后及分娩方式密切相关。CDFI诊断脐带扭转声像特征更直观、准确。脐动脉血流S/D值可提示胎儿缺氧和预后,有重要临床应用价值。     

Standardization of three-dimensional images in obstetrics and gynecology: consensus statement.

Standardization of the display of ultrasound images has so far only been achieved in transabdominal two-dimensional (2D) sonography. In contrast, there is a lack of uniformity in the demonstration of transvaginal 2D ultrasound images. The described non-uniformity frequently leads to confusion in the assessment of an image, in particular with regard to the accurate anatomical assignment of left/right and dorsal/ventral. Three-dimensional (3D) sonography offers a unique opportunity to avoid this confusion in the interpretation of ultrasound images, because, independent of primary volume acquisition, the volume can always be rotated so that the stored object can at all times be visualized in a known anatomical position, rendering it of no importance whether the image acquired transvaginally is demonstrated from above or from below. This will also be important in allowing fusion of ultrasound image data with computed tomographic, magnetic resonance and/or positron emission tomography images. In this article we suggest that standardization of transabdominal and transvaginal 3D images does not only provide the inexperienced physician/sonographer with a guide to spatial orientation, but also serves to avoid erroneous topographical interpretations.     

3D prostate model formation from non-parallel 2D ultrasound biopsy images

Biopsy of the prostate using 2D transrectal ultrasound (TRUS) guidance is the current gold standard for diagnosis of prostate cancer; however, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. We propose a technique for patient-specific 3D prostate model reconstruction from a sparse collection of non-parallel 2D TRUS biopsy images. Our method conforms to the restrictions of current TRUS biopsy equipment and could be efficiently incorporated into current clinical biopsy procedures for needle guidance without the need for expensive hardware additions. In this paper, the model reconstruction technique is evaluated using simulated biopsy images from 3D TRUS prostate images of 10 biopsy patients. All reconstructed models are compared to their corresponding 3D manually segmented prostate models for evaluation of prostate volume accuracy and surface errors (both regional and global). The number of 2D TRUS biopsy images used for prostate modeling was varied to determine the optimal number of images necessary for accurate prostate surface estimation.     

Prenatal detection of fetal growth restriction by fetal femur volume: efficacy assessment using three-dimensional ultrasound

As fetal growth restriction (FGR) may have increased risks with perinatal morbidity and mortality, it is very important to detect FGR prenatally. Fetal femur dysplasia is associated with a variety of congenital syndromes and FGR as well. To date, no prenatal assessment of fetal FV in predicting FGR using three-dimensional (3D) ultrasound (US) has been reported. In this study, we used 3D US to test the efficacy of fetal femur volume (FV) measurement in predicting FGR. We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and efficacy of fetal FV assessed by 3D US in detecting FGR according to the Bayes' theorem. All the fetuses were singletons and were followed up to delivery to determine whether they were complicated with FGR or not. In total, 304 fetuses without FGR and 42 fetuses with FGR were included for FV assessment in utero by 3D US. Our results showed fetal FV assessed by 3D US can differentiate fetuses with FGR from fetuses without FGR well. The best predicting threshold for FGR is at the 10th percentile of FV. Using the 10th percentile as the cutoff, the sensitivity of fetal FV in predicting FGR was 71.4%, specificity 94.1%, positive predictive value 62.5%, negative predictive value 96.0% and accuracy 91.3%. In addition, fetal FV is superior to fetal biparietal diameter and fetal abdominal circumference in predicting FGR. In conclusion, fetal FV assessed by 3D US can be applied to detect FGR well prenatally. We believe fetal FV assessment by 3D US would be a useful test in detecting fetuses with FGR.     

A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints

During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment.