Browsing by Subject "Semantic segmentation"

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    Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning
    (MDPI, 2021) Ji, Hyon Wook; Yoo, Sung Soo; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and Technology
    The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.
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    Understanding metric-related pitfalls in image analysis validation
    (ArXiv, 2023-09-25) Reinke, Annika; Tizabi, Minu D.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A. Emre; Rädsch, Tim; Sudre, Carole H.; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Blaschko, Matthew B.; Buettner, Florian; Cardoso, M. Jorge; Cheplygina, Veronika; Chen, Jianxu; Christodoulou, Evangelia; Cimini, Beth A.; Collins, Gary S.; Farahani, Keyvan; Ferrer, Luciana; Galdran, Adrian; Van Ginneken, Bram; Glocker, Ben; Godau, Patrick; Haase, Robert; Hashimoto, Daniel A.; Hoffman, Michael M.; Huisman, Merel; Isensee, Fabian; Jannin, Pierre; Kahn, Charles E.; Kainmueller, Dagmar; Kainz, Bernhard; Karargyris, Alexandros; Karthikesalingam, Alan; Kenngott, Hannes; Kleesiek, Jens; Kofler, Florian; Kooi, Thijs; Kopp-Schneider, Annette; Kozubek, Michal; Kreshuk, Anna; Kurc, Tahsin; Landman, Bennett A.; Litjens, Geert; Madani, Amin; Maier-Hein, Klaus; Martel, Anne L.; Mattson, Peter; Meijering, Erik; Menze, Bjoern; Moons, Karel G. M.; Müller, Henning; Nichyporuk, Brennan; Nickel, Felix; Petersen, Jens; Rafelski, Susanne M.; Rajpoot, Nasir; Reyes, Mauricio; Riegler, Michael A.; Rieke, Nicola; Saez-Rodriguez, Julio; Sánchez, Clara I.; Shetty, Shravya; Summers, Ronald M.; Taha, Abdel A.; Tiulpin, Aleksei; Tsaftaris, Sotirios A.; Van Calster, Ben; Varoquaux, Gaël; Yaniv, Ziv R.; Jäger, Paul F.; Maier-Hein, Lena; Pathology and Laboratory Medicine, School of Medicine
    Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.