智能超声扫查与细胞学筛查
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摘要
产科超声检查和妇科细胞学检查是妇产科常用的检查技术。其中,产前超声诊断的关键任务是获取感兴趣解剖结构的标准切面图像和进行相应的生物学测量,然而,这两个任务的完成质量依赖于超声医师的技巧和经验；宫颈细胞学筛查需细胞学技师使用显微镜从整张涂片中人工搜寻异常细胞,计算机辅助细胞检测系统(CCT)则能自动挑选出异常细胞,然而,目前的CCT尚未在临床上广泛使用。
为降低产前超声诊断结果的用户依赖性,本论文提出“智能超声扫查”理念,旨在基于图像分析方法实现早孕囊标准切面(SPGS)定位及生物学测量的自动化；为推进CCT的临床应用,本论文提出对手工液基细胞学(MLBC)制片结合苏木素-伊红(H&E)染色的涂片进行“智能细胞学筛查”,旨在表明该系统的筛查性能与针对自动液基细胞学制片(ALBC)结合专用染色的CCT具有可比性。
本论文设计的智能超声扫查算法框架分三步：首先,按由粗到精的检测策略,用两个级连AdaBoost分类器从超声序列图像中快速准确地定位出候选孕囊；然后利用序列图像中解剖结构之间的相对位置关系,排除假阳性检测结果,并选出SPGS;最后,提出一种数据库引导的多尺度标准割算法,用于获取孕囊的初始轮廓,并基于此,使用改进的蛇模型修正孕囊轮廓,进而得到测量结果。上述算法测试于来自31位孕妇的31段超声视频,结果显示,系统和超声医师在SPGS选择、长径测量、前后径测量等三个方面的差异分别为：7.5%±5.0%、5.5%±5.2%、6.5%±4.6%。进一步的验证表明,智能超声扫查的精度在医生间的差异范围内。因此,本论文认为,对二维超声影像中的早孕囊进行智能扫查,是一项可行的、可重复的和可靠的方法。此外,本论文提出的智能超声扫查算法框架经扩展后,有望应用到其他胎儿解剖结构的扫查任务中。
本论文对智能细胞学筛查系统的研究工作主要集中在自动细胞分割和分类两个方面。在分割方面：1)提出结合A*通道与混合多类分割的方法,能准确分割H&E染色图像中的细胞质；2)提出适用于异常细胞核分割的局部自适应图割算法；3)联合两种基于凹点对的分离算法有效分离粘连细胞核。在分类方面：1)设计了一个基于监督学习的宫颈细胞分类框架；2)新加入的粗糙度索引和局部二值模式(LBP)均值等特征能有效排除杂质和正常细胞；3)采用特征预处理技术提高分类器识别异常细胞的敏感性；4)利用细胞核面积上下文信息和近似的细胞质特征提高识别正常细胞的特异性。细胞病理学家使用智能细胞学筛查系统对43张涂片(21例异常,22例正常)做初筛,取得88.1%的敏感性和100%的特异性。初学者使用智能细胞学筛查系统做复筛,能找出8.3%的疑似异常涂片,这其中有25%为假阴性。参考商业CCT的性能可知,智能细胞学筛查系统具有高敏感性和高特异性,这说明对H&E染色的MLBC涂片进行智能筛查是可行的,此结论有望使CCT获得更广泛的应用。
Obstetrics ultrasound examination and gynecology cytology examination are common used examination techniques in obstetrics and gynecology. Among them, the key task of prenatal ultrasound diagnosis is to obtain the image of standard plane of interested anatomies and to perform biometric measurements. However, the accom-plishments of these tasks mainly depend on the experiences and skils of the radiolo-gist. The standard for screening cervical cytology is for a cytoscreener to manually search across an entire smear for abnormal cells using a conventional microscope. The computer-assisted cytological test (CCT) can automatically select abnormal cells, which allows targeted reading by cytoscreeners. However, the CCT has not been used widly in clinic at present.
To reduce the user dependency of prenatal ultrasound diagnosis results, we pro-pose an idea of Intelligent Ultrasound Scanning (IUS), which aims at automatically locating of the standard plane of early gestational sac (SPGS) and performing bio-metric measurements based on image analysis methods. To promote the clinical ap-plication of CCT, we propose to perform Intelligent Cytology Screening (ICS) on hematoxylin and eosin (H&E) stained manual liquid-based cytology (MLBC) slides. The aim is to verify the performance of ICS system is comparable to that of CCT with proprietary stained automated LBC (ALBC) slides.
The proposed intelligent ultrasound scanning (IUS) algorithm framework con-sists of three steps. First, the GS is efficiently and precisely located in each ultrasound frame by exploiting a coarse-to-fine detection scheme based on the training of two cascade AdaBoost classifiers. Next, the SPGS are automatically selected by eliminat-ing false positives. This is accomplished using local context information based on the relative position of anatomies in the image sequence. Finally, a database-guided mul-tiscale normalized cuts algorithm is proposed to generate the initial contour of the GS, based on which the GS is automatically segmented for measurement by a modified snake model. This system was validated on31ultrasound videos involving31preg- nant volunteers. The differences between system performance and radiologist perfor-mance with respect to SPGS selection and length and depth (diameter) measurements are7.5%±5.0%,5.5%±5.2%and6.5%±4.6%, respectively. Additional validations prove that the IUS precision is in the range of inter-observer variability. We conclude that IUS of the GS from2D real-time ultrasound is a practical, reproducible and relia-ble approach. In addition, after some extension, the proposed IUS algorithm frame-work has the potential to be applied to the scanning task of other fetal anatomies.
Our research on ICS sytem mainly focuses on the automatic cell segmentation and classification. Cell segmentation:1) the proposed method combining the A*channel and the hybrid multi-way cuts can accurately segment the cytoplasm on H&E staining images;2) the proposed Local Adaptive Graph Cuts algorithm can improve the segmentation precision of cell nuclei, especially abnormal cell nuclei; and3) the combination of two concave-based splitting algorithms can effectively split overlap-ping cell nuclei. Cell classification:1) we design a supervised learning-based cyto-logical classification framework;2) the newly incorporated features including rough-ness indexes and local binary pattern (LBP) means can effectively eliminate artifacts and normal cells;3) the sensitivity of classifiers to discriminating abnormal cells is increased by using feature preprocessing technique; and4) the specificity of normal cell recognition is improved by incorporating cell nuclei area context information and by using approximate cytoplasm features to exclude difficult negatives. Cyto-pathologists can get a sensitivity of88.1%and a specificity of100%when using the ICS system for prescreening on43smears (21abnormal and22normal). Moreover, for rescreening, beginner can find out8.3%suspected abnormal smears from those diagnosed as normal by cytopathologists, of which there is25%false negative rate. Clinical trials show that both the sensitivity and the specificity of the ICS system are satisfyingly high, thus proving the feasibility of the ICS for MLBC with H&E stain-ing. Therefore, the current research may lead to wider application of the CCT tech-nique.
为降低产前超声诊断结果的用户依赖性,本论文提出“智能超声扫查”理念,旨在基于图像分析方法实现早孕囊标准切面(SPGS)定位及生物学测量的自动化；为推进CCT的临床应用,本论文提出对手工液基细胞学(MLBC)制片结合苏木素-伊红(H&E)染色的涂片进行“智能细胞学筛查”,旨在表明该系统的筛查性能与针对自动液基细胞学制片(ALBC)结合专用染色的CCT具有可比性。
本论文设计的智能超声扫查算法框架分三步：首先,按由粗到精的检测策略,用两个级连AdaBoost分类器从超声序列图像中快速准确地定位出候选孕囊；然后利用序列图像中解剖结构之间的相对位置关系,排除假阳性检测结果,并选出SPGS;最后,提出一种数据库引导的多尺度标准割算法,用于获取孕囊的初始轮廓,并基于此,使用改进的蛇模型修正孕囊轮廓,进而得到测量结果。上述算法测试于来自31位孕妇的31段超声视频,结果显示,系统和超声医师在SPGS选择、长径测量、前后径测量等三个方面的差异分别为：7.5%±5.0%、5.5%±5.2%、6.5%±4.6%。进一步的验证表明,智能超声扫查的精度在医生间的差异范围内。因此,本论文认为,对二维超声影像中的早孕囊进行智能扫查,是一项可行的、可重复的和可靠的方法。此外,本论文提出的智能超声扫查算法框架经扩展后,有望应用到其他胎儿解剖结构的扫查任务中。
本论文对智能细胞学筛查系统的研究工作主要集中在自动细胞分割和分类两个方面。在分割方面：1)提出结合A*通道与混合多类分割的方法,能准确分割H&E染色图像中的细胞质；2)提出适用于异常细胞核分割的局部自适应图割算法；3)联合两种基于凹点对的分离算法有效分离粘连细胞核。在分类方面：1)设计了一个基于监督学习的宫颈细胞分类框架；2)新加入的粗糙度索引和局部二值模式(LBP)均值等特征能有效排除杂质和正常细胞；3)采用特征预处理技术提高分类器识别异常细胞的敏感性；4)利用细胞核面积上下文信息和近似的细胞质特征提高识别正常细胞的特异性。细胞病理学家使用智能细胞学筛查系统对43张涂片(21例异常,22例正常)做初筛,取得88.1%的敏感性和100%的特异性。初学者使用智能细胞学筛查系统做复筛,能找出8.3%的疑似异常涂片,这其中有25%为假阴性。参考商业CCT的性能可知,智能细胞学筛查系统具有高敏感性和高特异性,这说明对H&E染色的MLBC涂片进行智能筛查是可行的,此结论有望使CCT获得更广泛的应用。
Obstetrics ultrasound examination and gynecology cytology examination are common used examination techniques in obstetrics and gynecology. Among them, the key task of prenatal ultrasound diagnosis is to obtain the image of standard plane of interested anatomies and to perform biometric measurements. However, the accom-plishments of these tasks mainly depend on the experiences and skils of the radiolo-gist. The standard for screening cervical cytology is for a cytoscreener to manually search across an entire smear for abnormal cells using a conventional microscope. The computer-assisted cytological test (CCT) can automatically select abnormal cells, which allows targeted reading by cytoscreeners. However, the CCT has not been used widly in clinic at present.
To reduce the user dependency of prenatal ultrasound diagnosis results, we pro-pose an idea of Intelligent Ultrasound Scanning (IUS), which aims at automatically locating of the standard plane of early gestational sac (SPGS) and performing bio-metric measurements based on image analysis methods. To promote the clinical ap-plication of CCT, we propose to perform Intelligent Cytology Screening (ICS) on hematoxylin and eosin (H&E) stained manual liquid-based cytology (MLBC) slides. The aim is to verify the performance of ICS system is comparable to that of CCT with proprietary stained automated LBC (ALBC) slides.
The proposed intelligent ultrasound scanning (IUS) algorithm framework con-sists of three steps. First, the GS is efficiently and precisely located in each ultrasound frame by exploiting a coarse-to-fine detection scheme based on the training of two cascade AdaBoost classifiers. Next, the SPGS are automatically selected by eliminat-ing false positives. This is accomplished using local context information based on the relative position of anatomies in the image sequence. Finally, a database-guided mul-tiscale normalized cuts algorithm is proposed to generate the initial contour of the GS, based on which the GS is automatically segmented for measurement by a modified snake model. This system was validated on31ultrasound videos involving31preg- nant volunteers. The differences between system performance and radiologist perfor-mance with respect to SPGS selection and length and depth (diameter) measurements are7.5%±5.0%,5.5%±5.2%and6.5%±4.6%, respectively. Additional validations prove that the IUS precision is in the range of inter-observer variability. We conclude that IUS of the GS from2D real-time ultrasound is a practical, reproducible and relia-ble approach. In addition, after some extension, the proposed IUS algorithm frame-work has the potential to be applied to the scanning task of other fetal anatomies.
Our research on ICS sytem mainly focuses on the automatic cell segmentation and classification. Cell segmentation:1) the proposed method combining the A*channel and the hybrid multi-way cuts can accurately segment the cytoplasm on H&E staining images;2) the proposed Local Adaptive Graph Cuts algorithm can improve the segmentation precision of cell nuclei, especially abnormal cell nuclei; and3) the combination of two concave-based splitting algorithms can effectively split overlap-ping cell nuclei. Cell classification:1) we design a supervised learning-based cyto-logical classification framework;2) the newly incorporated features including rough-ness indexes and local binary pattern (LBP) means can effectively eliminate artifacts and normal cells;3) the sensitivity of classifiers to discriminating abnormal cells is increased by using feature preprocessing technique; and4) the specificity of normal cell recognition is improved by incorporating cell nuclei area context information and by using approximate cytoplasm features to exclude difficult negatives. Cyto-pathologists can get a sensitivity of88.1%and a specificity of100%when using the ICS system for prescreening on43smears (21abnormal and22normal). Moreover, for rescreening, beginner can find out8.3%suspected abnormal smears from those diagnosed as normal by cytopathologists, of which there is25%false negative rate. Clinical trials show that both the sensitivity and the specificity of the ICS system are satisfyingly high, thus proving the feasibility of the ICS for MLBC with H&E stain-ing. Therefore, the current research may lead to wider application of the CCT tech-nique.
引文
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