ABSTRACT
During evaluation of CT or MR images radiologists navigate through a volume in different orientations in order to detect a disease. While doing so, they leave a trail, which might hold valuable information for other clinicians. Unfortunately, current systems do not analyze this trail for certain motions or potential patterns. In this work we developed and implemented different strategies to infer the manifestation of a disease from the trail of inspection. Furthermore we evaluate the effectiveness of these strategies by conducting an experiment in which clinicians had to find a tumor in several cases. The results suggest that inferring suspicious areas from the trail is possible.
- Filipe R Barra, Renato R. Barra, and Alaor B. Sobrinho. 2010. Freeware medical image viewers: can we rely only on them?. Radiol Bras. 43:313--318.Google ScholarCross Ref
- Roland Ellerweg, Dominic Reuter, and Phil Weir. 2016. Architecture of a web-based DICOM viewer showing segmentations and simulations. In Proceedings of the 18th International Conference on eHealth Networking, Applications and Services (Healthcom'16), 1--5.Google ScholarCross Ref
- Roland Ellerweg, Dominic. Reuter, Elmar Stärk, Phil Weir. (2016). Design & Implementation of an on demand loading web based Dicom viewer showing anatomical structures. International Journal of Computer Assisted Radiology and Surgery. 11(1) pp. 178--179.Google Scholar
- Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Dino Pedreschi, and Fosca Giannotti. 2015. Retrieving points of interest from human systematic movements. Lecture Notes in Computer Science, 294--308.Google Scholar
- Daniel Haak, Charles E. Page, Klaus Kabino, and Thomas M. Deserno. 2015. Evaluation of DICOM viewer software for workflow integration in clinical trials. In SPIE 9418 Medical Imaging, pp. 94180O--94180O-9.Google Scholar
- Yuan N. Jing, Zheng Yu, Zhang Liuhang, Xie Xing, and Sun Guangzhong. 2011. Where to find my next passenger? In Proceedings of the 13th International Conference on Ubiquitous Computing. UbiComp'11, 109--118. Google ScholarDigital Library
- Henrik Kretzschmar, Markus Kuderer, and Wolfram Burgard. 2014. Learning to Predict Trajectories of Cooperatively Navigating Agents. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'14), 4015--4020.Google ScholarCross Ref
- Quannan Li, Yu Zheng, Xing Xie, Yukun Chen, Wenyu Liu, and Wei-Ying Ma. 2008. Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (GIS'08). 34:1--34:10. Google ScholarDigital Library
- Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. 2009. WhereNext: A Location Predictor on Trajectory Pattern Mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15), 637--646. Google ScholarDigital Library
- Susanta Satpathy, Lokesh Sharma, Ajaya K. Akasapu and Netreshwari Sharma. 2011. Towards Mining Approaches for Trajectory Data. International Journal of Advances in Science and Technology 2, 3: 38--43.Google Scholar
- Md Reaz Uddin, Chinya Ravishankar, Vassilis J. Tsotras. 2011. Finding Regions of Interest from Trajectory Data. In Proceedings of the IEEE International Conference on Mobile Data Management, 39--48. Google ScholarDigital Library
- Xing Xie, Yu Zheng, Liuhang Zhang, and Nicholas J. Yuan. 2013. T-Finder: A Recommender System for Finding Passengers and Vacant Taxis. J. IEEE Transactions on Knowledge & Data Engineering. 25, 2390--2403. Google ScholarDigital Library
- Yang Ye, Yu Zheng, Yukun Chen, Jianhua Feng, Xing Xie. 2009. Mining Individual Life Pattern Based on Location History. In Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware. MDM '09, 1--10. Google ScholarDigital Library
- Yu Zheng. 2015. Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology (TIST) 6, 13: 1--41. Google ScholarDigital Library
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