The Experts below are selected from a list of 4914 Experts worldwide ranked by ideXlab platform
Heinrich D. Becker - One of the best experts on this subject based on the ideXlab platform.
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Computer-Assisted Diagnosis for White Light Bronchoscopy: First Results
Chest, 2010Co-Authors: Michaela Benz, Andreas Kage, Christian Münzenmayer, Jose Rojas-solano, T Wittenberg, Heinrich D. BeckerAbstract:Computer-Assisted Diagnosis (CAD) systems are able to support physicians with the interpretation of complex image data acquired from modalities such as computed tomography scans (CT). CAD can provide an objective second opinion or detect suspicious regions automatically. However, few CAD systems for lung diseases based in other image modalities than CT, such as bronchoscopy, have been described.1-3 We present the results of our proposed CAD system based on white light bronchoscopy (WLB) images
Yi Cai - One of the best experts on this subject based on the ideXlab platform.
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Computer-Assisted Diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy.
Endoscopy, 2018Co-Authors: Yuanyuan Zhao, Di-xiu Xue, Ya-lei Wang, Rong Zhang, Bin Sun, Yong-ping Cai, Hui Feng, Yi CaiAbstract:Background We developed a Computer-Assisted Diagnosis model to evaluate the feasibility of automated classification of intrapapillary capillary loops (IPCLs) to improve the detection of esophageal squamous cell carcinoma (ESCC). Methods We recruited patients who underwent magnifying endoscopy with narrow-band imaging for evaluation of a suspicious esophageal condition. Case images were evaluated to establish a gold standard IPCL classification according to the endoscopic Diagnosis and histological findings. A double-labeling fully convolutional network (FCN) was developed for image segmentation. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior > 15 years; mid level 10 – 15 years; junior 5 – 10 years). Results Of the 1383 lesions in the study, the mean accuracies of IPCL classification were 92.0 %, 82.0 %, and 73.3 %, for the senior, mid level, and junior groups, respectively. The mean diagnostic accuracy of the model was 89.2 % and 93.0 % at the lesion and pixel levels, respectively. The interobserver agreement between the model and the gold standard was substantial (kappa value, 0.719). The accuracy of the model for inflammatory lesions (92.5 %) was superior to that of the mid level (88.1 %) and junior (86.3 %) groups (P Conclusions Double-labeling FCN automated IPCL recognition was feasible and could facilitate early detection of ESCC.
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Computer-Assisted Diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy.
Endoscopy, 2018Co-Authors: Yuanyuan Zhao, Di-xiu Xue, Ya-lei Wang, Rong Zhang, Bin Sun, Yong-ping Cai, Hui Feng, Yi CaiAbstract:We developed a Computer-Assisted Diagnosis model to evaluate the feasibility of automated classification of intrapapillary capillary loops (IPCLs) to improve the detection of esophageal squamous cell carcinoma (ESCC). We recruited patients who underwent magnifying endoscopy with narrow-band imaging for evaluation of a suspicious esophageal condition. Case images were evaluated to establish a gold standard IPCL classification according to the endoscopic Diagnosis and histological findings. A double-labeling fully convolutional network (FCN) was developed for image segmentation. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior > 15 years; mid level 10 - 15 years; junior 5 - 10 years). Of the 1383 lesions in the study, the mean accuracies of IPCL classification were 92.0 %, 82.0 %, and 73.3 %, for the senior, mid level, and junior groups, respectively. The mean diagnostic accuracy of the model was 89.2 % and 93.0 % at the lesion and pixel levels, respectively. The interobserver agreement between the model and the gold standard was substantial (kappa value, 0.719). The accuracy of the model for inflammatory lesions (92.5 %) was superior to that of the mid level (88.1 %) and junior (86.3 %) groups (P < 0.001). For malignant lesions, the accuracy of the model (B1, 87.6 %; B2, 93.9 %) was significantly higher than that of the mid level (B1, 79.1 %; B2, 90.0 %) and junior (B1, 69.2 %; B2, 79.3 %) groups (P < 0.001). Double-labeling FCN automated IPCL recognition was feasible and could facilitate early detection of ESCC. © Georg Thieme Verlag KG Stuttgart · New York.
Christian Münzenmayer - One of the best experts on this subject based on the ideXlab platform.
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a comparison of basic deinterlacing approaches for a Computer Assisted Diagnosis approach of videoscope images
Proceedings of SPIE, 2010Co-Authors: Andreas Kage, Marcia I Canto, Emmanuel C Gorospe, Antonio Almario, Christian MünzenmayerAbstract:In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the Diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for Computer Assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the Diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.
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Medical Imaging: Computer-Aided Diagnosis - A comparison of basic deinterlacing approaches for a Computer Assisted Diagnosis approach of videoscope images
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Andreas Kage, Marcia I Canto, Emmanuel C Gorospe, Antonio Almario, Christian MünzenmayerAbstract:In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the Diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for Computer Assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the Diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.
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Computer-Assisted Diagnosis for White Light Bronchoscopy: First Results
Chest, 2010Co-Authors: Michaela Benz, Andreas Kage, Christian Münzenmayer, Jose Rojas-solano, T Wittenberg, Heinrich D. BeckerAbstract:Computer-Assisted Diagnosis (CAD) systems are able to support physicians with the interpretation of complex image data acquired from modalities such as computed tomography scans (CT). CAD can provide an objective second opinion or detect suspicious regions automatically. However, few CAD systems for lung diseases based in other image modalities than CT, such as bronchoscopy, have been described.1-3 We present the results of our proposed CAD system based on white light bronchoscopy (WLB) images
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Computer-Assisted Diagnosis for precancerous lesions in the esophagus.
Methods of information in medicine, 2009Co-Authors: Christian Münzenmayer, Andreas Kage, Thomas Wittenberg, S MühldorferAbstract:Objectives: The interpretation of endoscopic findings by gastroenterologists is still a difficult and highly subjective task. Despite important developments such as chromo-endoscopy, pit pattern analysis, fluorescence imaging as well as narrow band imaging it still requires lots of experience and training with a certain tentativeness until the final biopsy. By the development of Computer-Assisted Diagnosis (CAD) systems this process can be supported. Methods: This paper presents a new approach to CAD for precancerous lesions in the esophagus based on color-texture analysis in a content-based image retrieval (CBIR) framework. The novelty of our approach lies in the combination of newly developed color-texture features with the interactive feedback loop provided by a relevance feedback algorithm. This allows the expert to steer the query and is still robust against accidental false decisions. Results: We reached an inter-rater reliability of k = 0.71 on a database of 390 endoscopic images. The retrieval accuracy didn’t change significantly until a wrong decision rate of 20%. Conclusions: Thus, the system could be able to support practitioners with less experience or in private practice. In combination with a connected case database it can also support case-based reasoning for the diagnostic decision process.
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Medical Imaging: Computer-Aided Diagnosis - Narrow-band imaging for the Computer Assisted Diagnosis in patients with Barrett's esophagus
Proceedings of SPIE, 2009Co-Authors: Andreas Kage, Steffen Zopf, Thomas Wittenberg, Martin Raithel, Christian MünzenmayerAbstract:Cancer of the esophagus has the worst prediction of all known cancers in Germany. The early detection of suspicious changes in the esophagus allows therapies that can prevent the cancer. Barrett's esophagus is a premalignant change of the esophagus that is a strong indication for cancer. Therefore there is a big interest to detect Barrett's esophagus as early as possible. The standard examination is done with a videoscope where the physician checks the esophagus for suspicious regions. Once a suspicious region is found, the physician takes a biopsy of that region to get a histological result of it. Besides the traditional white light for the illumination there is a new technology: the so called narrow-band Imaging (NBI). This technology uses a smaller spectrum of the visible light to highlight the scene captured by the videoscope. Medical studies indicate that the use of NBI instead of white light can increase the rate of correct diagnoses of a physician. In the future, Computer-Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support the physician in the Diagnosis of different lesions in the esophagus. A knowledge-based system which uses a database is a possible solution for this task. For our work we have collected NBI images containing 326 Regions of Interest (ROI) of three typical classes: epithelium, cardia mucosa and Barrett's esophagus. We then used standard texture analysis features like those proposed by Haralick, Chen, Gabor and Unser to extract features from every ROI. The performance of the classification was evaluated with a classifier using the leaving-one-out sampling. The best result that was achieved is an accuracy of 92% for all classes and an accuracy of 76% for Barrett's esophagus. These results show that the NBI technology can provide a good Diagnosis support when used in a CAD system.
Andreas Kage - One of the best experts on this subject based on the ideXlab platform.
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a comparison of basic deinterlacing approaches for a Computer Assisted Diagnosis approach of videoscope images
Proceedings of SPIE, 2010Co-Authors: Andreas Kage, Marcia I Canto, Emmanuel C Gorospe, Antonio Almario, Christian MünzenmayerAbstract:In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the Diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for Computer Assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the Diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.
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Medical Imaging: Computer-Aided Diagnosis - A comparison of basic deinterlacing approaches for a Computer Assisted Diagnosis approach of videoscope images
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Andreas Kage, Marcia I Canto, Emmanuel C Gorospe, Antonio Almario, Christian MünzenmayerAbstract:In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the Diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for Computer Assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the Diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.
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Computer-Assisted Diagnosis for White Light Bronchoscopy: First Results
Chest, 2010Co-Authors: Michaela Benz, Andreas Kage, Christian Münzenmayer, Jose Rojas-solano, T Wittenberg, Heinrich D. BeckerAbstract:Computer-Assisted Diagnosis (CAD) systems are able to support physicians with the interpretation of complex image data acquired from modalities such as computed tomography scans (CT). CAD can provide an objective second opinion or detect suspicious regions automatically. However, few CAD systems for lung diseases based in other image modalities than CT, such as bronchoscopy, have been described.1-3 We present the results of our proposed CAD system based on white light bronchoscopy (WLB) images
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Computer-Assisted Diagnosis for precancerous lesions in the esophagus.
Methods of information in medicine, 2009Co-Authors: Christian Münzenmayer, Andreas Kage, Thomas Wittenberg, S MühldorferAbstract:Objectives: The interpretation of endoscopic findings by gastroenterologists is still a difficult and highly subjective task. Despite important developments such as chromo-endoscopy, pit pattern analysis, fluorescence imaging as well as narrow band imaging it still requires lots of experience and training with a certain tentativeness until the final biopsy. By the development of Computer-Assisted Diagnosis (CAD) systems this process can be supported. Methods: This paper presents a new approach to CAD for precancerous lesions in the esophagus based on color-texture analysis in a content-based image retrieval (CBIR) framework. The novelty of our approach lies in the combination of newly developed color-texture features with the interactive feedback loop provided by a relevance feedback algorithm. This allows the expert to steer the query and is still robust against accidental false decisions. Results: We reached an inter-rater reliability of k = 0.71 on a database of 390 endoscopic images. The retrieval accuracy didn’t change significantly until a wrong decision rate of 20%. Conclusions: Thus, the system could be able to support practitioners with less experience or in private practice. In combination with a connected case database it can also support case-based reasoning for the diagnostic decision process.
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Medical Imaging: Computer-Aided Diagnosis - Narrow-band imaging for the Computer Assisted Diagnosis in patients with Barrett's esophagus
Proceedings of SPIE, 2009Co-Authors: Andreas Kage, Steffen Zopf, Thomas Wittenberg, Martin Raithel, Christian MünzenmayerAbstract:Cancer of the esophagus has the worst prediction of all known cancers in Germany. The early detection of suspicious changes in the esophagus allows therapies that can prevent the cancer. Barrett's esophagus is a premalignant change of the esophagus that is a strong indication for cancer. Therefore there is a big interest to detect Barrett's esophagus as early as possible. The standard examination is done with a videoscope where the physician checks the esophagus for suspicious regions. Once a suspicious region is found, the physician takes a biopsy of that region to get a histological result of it. Besides the traditional white light for the illumination there is a new technology: the so called narrow-band Imaging (NBI). This technology uses a smaller spectrum of the visible light to highlight the scene captured by the videoscope. Medical studies indicate that the use of NBI instead of white light can increase the rate of correct diagnoses of a physician. In the future, Computer-Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support the physician in the Diagnosis of different lesions in the esophagus. A knowledge-based system which uses a database is a possible solution for this task. For our work we have collected NBI images containing 326 Regions of Interest (ROI) of three typical classes: epithelium, cardia mucosa and Barrett's esophagus. We then used standard texture analysis features like those proposed by Haralick, Chen, Gabor and Unser to extract features from every ROI. The performance of the classification was evaluated with a classifier using the leaving-one-out sampling. The best result that was achieved is an accuracy of 92% for all classes and an accuracy of 76% for Barrett's esophagus. These results show that the NBI technology can provide a good Diagnosis support when used in a CAD system.
Vicente Moret-bonillo - One of the best experts on this subject based on the ideXlab platform.
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Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review
Sleep disorders, 2015Co-Authors: Diego Álvarez-estévez, Vicente Moret-bonilloAbstract:Automatic Diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual Diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of Computer-Assisted Diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.
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EMBC - Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: An overview of different approaches
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2015Co-Authors: Diego Álvarez-estévez, Vicente Moret-bonilloAbstract:Automatic Diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine, and the costs associated to its manual Diagnosis. The increment and heterogeneity of the different techniques, however, makes somewhat difficult to adequately follow recent developments. In this paper an overview within the area of Computer-Assisted Diagnosis of SAHS has been performed. This overview of the different methods is presented together with a critical discussion of the current state-of-the-art.