AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased cl...AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.展开更多
Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize...Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize building performance.The model structures for CAD and BEM are divergent.In this study,geometry transformation methods was implemented in BES tools for the early design stage,including auto space generation(ASG)method based on closed contour recognition(CCR)and space boundary topology calculation method.The program is developed based on modeling tools SketchUp to support the CAD format(like*.stl,*.dwg,*.ifc,etc.).It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements.In addition,this study provided a space topology calculation method based on a single-zone BEM output.The program was developed based on the SketchUp modeling tool to support additional CAD formats(such as*.stl,*.dwg,*.ifc),which can then be imported and transformed into*.obj.Compared to current methods mostly focused on BIM-BEM transformation,this method can ensure more modeling flexibility.The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program.They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions.It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.展开更多
The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the ext...The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.展开更多
基金Supported by the Austrian Science Fund(FWF),No.KLI 429-B13 to Vécsei A
文摘AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.
基金We would like to thank the National Science Foundation of China(Grant No.52130803)for funding this study.
文摘Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize building performance.The model structures for CAD and BEM are divergent.In this study,geometry transformation methods was implemented in BES tools for the early design stage,including auto space generation(ASG)method based on closed contour recognition(CCR)and space boundary topology calculation method.The program is developed based on modeling tools SketchUp to support the CAD format(like*.stl,*.dwg,*.ifc,etc.).It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements.In addition,this study provided a space topology calculation method based on a single-zone BEM output.The program was developed based on the SketchUp modeling tool to support additional CAD formats(such as*.stl,*.dwg,*.ifc),which can then be imported and transformed into*.obj.Compared to current methods mostly focused on BIM-BEM transformation,this method can ensure more modeling flexibility.The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program.They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions.It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.
文摘The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.
