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Author |
Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie |
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Title |
Inferring the Performance of Medical Imaging Algorithms |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
6854 |
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Pages |
520-528 |
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Keywords  |
Validation, Statistical Inference, Medical Imaging Algorithms. |
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Abstract |
Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence. |
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Address |
Sevilla |
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Publisher |
Springer-Verlag Berlin Heidelberg |
Place of Publication |
Berlin |
Editor |
Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch |
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LNCS |
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CAIP |
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Notes |
IAM; ADAS |
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no |
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Call Number |
IAM @ iam @ HGR2011 |
Serial |
1676 |
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Author |
Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez |
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Title |
Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance |
Type |
Conference Article |
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Year |
2013 |
Publication |
Proceedings of the International Conference on Computer Vision Theory and Applications |
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Volume |
1 |
Issue |
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Pages |
153--161 |
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Keywords |
Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model |
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Abstract |
Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability |
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Address |
Barcelona; February 2013 |
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Publisher |
SciTePress |
Place of Publication |
Portugal |
Editor |
Sebastiano Battiato and José Braz |
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LNCS |
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ISBN |
978-989-8565-47-1 |
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800 |
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Conference |
VISAPP |
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Notes |
IAM;MV; 600.044; 600.047; 600.060; 605.203 |
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no |
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Call Number |
IAM @ iam @ SGR2013 |
Serial |
2123 |
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Author |
Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Towards a Videobronchoscopy Localization System from Airway Centre Tracking |
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Conference Article |
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Year |
2017 |
Publication |
12th International Conference on Computer Vision Theory and Applications |
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Pages |
352-359 |
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Keywords |
Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation |
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Abstract |
Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations. |
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Porto; Portugal; February 2017 |
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VISAPP |
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Notes |
IAM; 600.096; 600.075; 600.145 |
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Call Number |
Admin @ si @ SEB2017 |
Serial |
2943 |
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Author |
Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
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Title |
BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation |
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Journal Article |
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Year |
2023 |
Publication |
Computer Methods and Programs in Biomedicine |
Abbreviated Journal |
CMPB |
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Volume |
228 |
Issue |
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Pages |
107241 |
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Keywords |
Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation |
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Abstract |
Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy. |
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Elsevier |
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IAM; |
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Call Number |
Admin @ si @ BSC2023 |
Serial |
3702 |
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