Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and b...Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and bronchoscopy,owing to its severe invasiveness.In comparison,virtual pancreatoscopy(VP)has shown notable advantages.However,because of the low resolution of current computed tomography(CT)technology and the small diameter of the pancreatic duct,VP has limited clinical use.In this study,an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer.The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04.Owing to the excellent segmentation performance,a fly-through visualization of both the inside and outside of the duct was successfully reconstructed,thereby demonstrating the feasibility of VP.In addition,a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization.The entire VP system developed in this study is available at https://github.com/gaoyi/VirtualEndoscopy.git.展开更多
Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging qu...Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.展开更多
基金This work is partially supported by the Key-Area Research and Development Program of Guangdong Province,No.2021B0101420005the Key Technology Development Program of Shenzhen,No.JSGG20210713091811036+4 种基金the Department of Education of Guangdong Province,No.2017KZDXM072the National Natural Science Foundation of China,No.61601302the Shenzhen Key Laboratory Foundation,No.ZDSYS20200811143757022the Shenzhen Peacock Plan,No.KQTD2016053112051497the SZU Top Ranking Project,No.86000000210.
文摘Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and bronchoscopy,owing to its severe invasiveness.In comparison,virtual pancreatoscopy(VP)has shown notable advantages.However,because of the low resolution of current computed tomography(CT)technology and the small diameter of the pancreatic duct,VP has limited clinical use.In this study,an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer.The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04.Owing to the excellent segmentation performance,a fly-through visualization of both the inside and outside of the duct was successfully reconstructed,thereby demonstrating the feasibility of VP.In addition,a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization.The entire VP system developed in this study is available at https://github.com/gaoyi/VirtualEndoscopy.git.
基金the National Natural Science Foundation of China (61571304, 81571758, and 61701312)the National Key Research and Development Program of China (2016YFC0104703)+1 种基金the Medical Scientific Research Foundation of Guangdong Province, China (B2018031)the Shenzhen Peacock Plan (KQTD2016053112051497).
文摘Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.
