Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computer-aided surgery. However,kidney segmentation from CT images is generally performed manua...Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computer-aided surgery. However,kidney segmentation from CT images is generally performed manually or semi-automatically because of gray levels similarities of adjacent organs/tissues in abdominal CT images. This paper presents an efficient algorithm for segmenting kidney from serials of abdominal CT images. First,we extracted estimated kidney position(EKP) according to the statistical geometric location of kidney within the abdomen. Second,we analyzed the intensity distribution of EKP for several abdominal CT images and exploit an adaptive threshold searching algorithm to eliminate many other organs/tissues in the EKP. Finally,a novel region growing approach based on labeling is used to obtain the fine kidney regions. Experimental results are comparable to those of manual tracing radiologist and shown to be efficient.展开更多
Objective Abdominal aortic balloon occlusion(AABO)is a vascular intervention method that has been widely used in the treatment of severe placenta accreta spectrum(PAS).The aim of this study was to investigate the bene...Objective Abdominal aortic balloon occlusion(AABO)is a vascular intervention method that has been widely used in the treatment of severe placenta accreta spectrum(PAS).The aim of this study was to investigate the benefits,potential risks,and characteristics of AABO combined with tourniquet binding of the lower uterine segment(LUS)in treatment of pregnant women with PAS.Methods In this study,64 pregnant women with PAS scores greater than 5 were enrolled as research subjects and divided into two groups.Group A(n=34)underwent normal operative procedures including tourniquet binding of the LUS.Group B(n=30)underwent AABO combined with tourniquet binding of the LUS.General clinical characteristics,ultrasonography PAS score,intraoperative blood loss(IBL),blood loss within 24 h after surgery(24-h BL),postoperative complications,and neonatal data of the two groups were retrospectively reviewed.The influencing factors of IBL for the two groups were analyzed.Results The amounts of IBL,24-h BL,total input red blood cell,and the incidence of disseminated intravascular coagulation were significantly lower in group B than in group A(P<0.05),and this difference was even more significant in the subgroup of placenta percreta(PAS scores≥10).Further multivariate linear analysis showed that the combined therapy of AABO and tourniquet could independently predict lower IBL than normal operative procedures did(P=0.001).Conclusion AABO combined with tourniquet binding of the LUS could improve the outcomes of pregnant women with severe PAS and reduce serious peripartum complications of AABO.展开更多
Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdo...Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater morbidity and mortality. Most patients often have asymptomatic abdominal conditions and symptoms, which are often recognized late;hence the abdomen has been the third most common cause of damage to the human body. That notwithstanding,there may be improved outcomes where the condition of an abdominal organ is detected earlier. Over the years, supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods perform well when the used training data represents the target data, but the methods require large manually annotated data and have adaptation problems. The semi-supervised methods are fast but record poor performance than the supervised if assumptions about the data fail to hold. Current state-of-the-art methods of supervised segmentation are largely based on deep learning techniques due to their good accuracy and success in real world applications. Though it requires a large amount of training data for automatic feature extraction, deep learning can hardly be used. As regards the semi-supervised methods of segmentation, self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrive on their convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches, connection and gaps are identified, and prospective future research opportunities are enumerated.展开更多
基金National Natural Science Foundations of China (No.60601025, No.60701022, No.30770561)
文摘Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computer-aided surgery. However,kidney segmentation from CT images is generally performed manually or semi-automatically because of gray levels similarities of adjacent organs/tissues in abdominal CT images. This paper presents an efficient algorithm for segmenting kidney from serials of abdominal CT images. First,we extracted estimated kidney position(EKP) according to the statistical geometric location of kidney within the abdomen. Second,we analyzed the intensity distribution of EKP for several abdominal CT images and exploit an adaptive threshold searching algorithm to eliminate many other organs/tissues in the EKP. Finally,a novel region growing approach based on labeling is used to obtain the fine kidney regions. Experimental results are comparable to those of manual tracing radiologist and shown to be efficient.
基金2018 Applied Medicine Research Projects of Health and Family Planning Commission of Hubei(No.WJ2018H0139 and No.WJ2018H0133).
文摘Objective Abdominal aortic balloon occlusion(AABO)is a vascular intervention method that has been widely used in the treatment of severe placenta accreta spectrum(PAS).The aim of this study was to investigate the benefits,potential risks,and characteristics of AABO combined with tourniquet binding of the lower uterine segment(LUS)in treatment of pregnant women with PAS.Methods In this study,64 pregnant women with PAS scores greater than 5 were enrolled as research subjects and divided into two groups.Group A(n=34)underwent normal operative procedures including tourniquet binding of the LUS.Group B(n=30)underwent AABO combined with tourniquet binding of the LUS.General clinical characteristics,ultrasonography PAS score,intraoperative blood loss(IBL),blood loss within 24 h after surgery(24-h BL),postoperative complications,and neonatal data of the two groups were retrospectively reviewed.The influencing factors of IBL for the two groups were analyzed.Results The amounts of IBL,24-h BL,total input red blood cell,and the incidence of disseminated intravascular coagulation were significantly lower in group B than in group A(P<0.05),and this difference was even more significant in the subgroup of placenta percreta(PAS scores≥10).Further multivariate linear analysis showed that the combined therapy of AABO and tourniquet could independently predict lower IBL than normal operative procedures did(P=0.001).Conclusion AABO combined with tourniquet binding of the LUS could improve the outcomes of pregnant women with severe PAS and reduce serious peripartum complications of AABO.
基金supported by National Natural Science Foundation of China(Nos.61772242,61976106 and 61572239)the China Postdoctoral Science Foundation(No.2017M611737)+1 种基金the Six Talent Peaks Project in Jiangsu Province(No.DZXX-122)the Key Special Project of Health and Family Planning Science and Technology in Zhenjiang City(No.SHW2017019)。
文摘Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater morbidity and mortality. Most patients often have asymptomatic abdominal conditions and symptoms, which are often recognized late;hence the abdomen has been the third most common cause of damage to the human body. That notwithstanding,there may be improved outcomes where the condition of an abdominal organ is detected earlier. Over the years, supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods perform well when the used training data represents the target data, but the methods require large manually annotated data and have adaptation problems. The semi-supervised methods are fast but record poor performance than the supervised if assumptions about the data fail to hold. Current state-of-the-art methods of supervised segmentation are largely based on deep learning techniques due to their good accuracy and success in real world applications. Though it requires a large amount of training data for automatic feature extraction, deep learning can hardly be used. As regards the semi-supervised methods of segmentation, self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrive on their convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches, connection and gaps are identified, and prospective future research opportunities are enumerated.
