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1. Understanding metric-related pitfalls in image analysis validation

Author

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, 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, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul F., and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

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., Comment: Shared first authors: Annika Reinke and Minu D. Tizabi; shared senior authors: Lena Maier-Hein and Paul F. J\"ager. Published in Nature Methods. arXiv admin note: text overlap with arXiv:2206.01653

Published
2023
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2. Metrics reloaded: recommendations for image analysis validation

Author

Maier-Hein, Lena, Reinke, Annika, Godau, Patrick, Tizabi, Minu D., Buettner, Florian, Christodoulou, Evangelia, Glocker, Ben, Isensee, Fabian, Kleesiek, Jens, Kozubek, Michal, Reyes, Mauricio, Riegler, Michael A., Wiesenfarth, Manuel, Kavur, A. Emre, Sudre, Carole H., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Rädsch, Tim, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew B., Cardoso, M. Jorge, Cheplygina, Veronika, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kofler, Florian, Kopp-Schneider, Annette, 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, Rajpoot, Nasir, Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, and Jäger, Paul F.

Published
2024
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3. Common Limitations of Image Processing Metrics: A Picture Story

Author

Reinke, Annika, Tizabi, Minu D., Sudre, Carole H., Eisenmann, Matthias, Rädsch, Tim, Baumgartner, Michael, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Bankhead, Peter, Benis, Arriel, Blaschko, Matthew, Buettner, Florian, Cardoso, M. Jorge, Chen, Jianxu, Cheplygina, Veronika, Christodoulou, Evangelia, Cimini, Beth, Collins, Gary S., Engelhardt, Sandy, Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Haase, Robert, Hamprecht, Fred, Hashimoto, Daniel A., Heckmann-Nötzel, Doreen, Hirsch, Peter, Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kavur, A. Emre, Kenngott, Hannes, Kleesiek, Jens, Kleppe, Andreas, Kohler, Sven, Kofler, Florian, Kopp-Schneider, Annette, Kooi, Thijs, 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, Moher, David, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Noyan, M. Alican, Petersen, Jens, Polat, Gorkem, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Rieke, Nicola, Riegler, Michael, Rivaz, Hassan, Saez-Rodriguez, Julio, Sánchez, Clara I., Schroeter, Julien, Saha, Anindo, Selver, M. Alper, Sharan, Lalith, Shetty, Shravya, van Smeden, Maarten, Stieltjes, Bram, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul, and Maier-Hein, Lena

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide., Comment: Shared first authors: Annika Reinke and Minu D. Tizabi. This is a dynamic paper on limitations of commonly used metrics. It discusses metrics for image-level classification, semantic and instance segmentation, and object detection. For missing use cases, comments or questions, please contact a.reinke@dkfz.de. Substantial contributions to this document will be acknowledged with a co-authorship

Published
2021

4. Surgical Data Science -- from Concepts toward Clinical Translation

Author

Maier-Hein, Lena, Eisenmann, Matthias, Sarikaya, Duygu, März, Keno, Collins, Toby, Malpani, Anand, Fallert, Johannes, Feussner, Hubertus, Giannarou, Stamatia, Mascagni, Pietro, Nakawala, Hirenkumar, Park, Adrian, Pugh, Carla, Stoyanov, Danail, Vedula, Swaroop S., Cleary, Kevin, Fichtinger, Gabor, Forestier, Germain, Gibaud, Bernard, Grantcharov, Teodor, Hashizume, Makoto, Heckmann-Nötzel, Doreen, Kenngott, Hannes G., Kikinis, Ron, Mündermann, Lars, Navab, Nassir, Onogur, Sinan, Sznitman, Raphael, Taylor, Russell H., Tizabi, Minu D., Wagner, Martin, Hager, Gregory D., Neumuth, Thomas, Padoy, Nicolas, Collins, Justin, Gockel, Ines, Goedeke, Jan, Hashimoto, Daniel A., Joyeux, Luc, Lam, Kyle, Leff, Daniel R., Madani, Amin, Marcus, Hani J., Meireles, Ozanan, Seitel, Alexander, Teber, Dogu, Ückert, Frank, Müller-Stich, Beat P., Jannin, Pierre, and Speidel, Stefanie

Subjects
Computer Science - Computers and Society, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Published
2020

5. Surgical data science – from concepts toward clinical translation

Author

Maier-Hein, Lena, Eisenmann, Matthias, Sarikaya, Duygu, März, Keno, Collins, Toby, Malpani, Anand, Fallert, Johannes, Feussner, Hubertus, Giannarou, Stamatia, Mascagni, Pietro, Nakawala, Hirenkumar, Park, Adrian, Pugh, Carla, Stoyanov, Danail, Vedula, Swaroop S., Cleary, Kevin, Fichtinger, Gabor, Forestier, Germain, Gibaud, Bernard, Grantcharov, Teodor, Hashizume, Makoto, Heckmann-Nötzel, Doreen, Kenngott, Hannes G., Kikinis, Ron, Mündermann, Lars, Navab, Nassir, Onogur, Sinan, Roß, Tobias, Sznitman, Raphael, Taylor, Russell H., Tizabi, Minu D., Wagner, Martin, Hager, Gregory D., Neumuth, Thomas, Padoy, Nicolas, Collins, Justin, Gockel, Ines, Goedeke, Jan, Hashimoto, Daniel A., Joyeux, Luc, Lam, Kyle, Leff, Daniel R., Madani, Amin, Marcus, Hani J., Meireles, Ozanan, Seitel, Alexander, Teber, Dogu, Ückert, Frank, Müller-Stich, Beat P., Jannin, Pierre, and Speidel, Stefanie

Published
2022
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6. Understanding metric-related pitfalls in image analysis validation

Author

Epi Methoden, Cancer, JC onderzoeksprogramma Methodology, 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, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, 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., 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, Epi Methoden, Cancer, JC onderzoeksprogramma Methodology, 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, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, 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., 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., and Maier-Hein, Lena

Published
2024

7. Metrics reloaded: recommendations for image analysis validation

Author

Epi Methoden, Cancer, JC onderzoeksprogramma Methodology, Infection & Immunity, Epi Methoden Team 3, Maier-Hein, Lena, Reinke, Annika, Godau, Patrick, Tizabi, Minu D., Buettner, Florian, Christodoulou, Evangelia, Glocker, Ben, Isensee, Fabian, Kleesiek, Jens, Kozubek, Michal, Reyes, Mauricio, Riegler, Michael A., Wiesenfarth, Manuel, Kavur, A. Emre, Sudre, Carole H., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Rädsch, Tim, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew B., Cardoso, M. Jorge, Cheplygina, Veronika, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kofler, Florian, Kopp-Schneider, Annette, 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, Rajpoot, Nasir, Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Jäger, Paul F., Epi Methoden, Cancer, JC onderzoeksprogramma Methodology, Infection & Immunity, Epi Methoden Team 3, Maier-Hein, Lena, Reinke, Annika, Godau, Patrick, Tizabi, Minu D., Buettner, Florian, Christodoulou, Evangelia, Glocker, Ben, Isensee, Fabian, Kleesiek, Jens, Kozubek, Michal, Reyes, Mauricio, Riegler, Michael A., Wiesenfarth, Manuel, Kavur, A. Emre, Sudre, Carole H., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Rädsch, Tim, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew B., Cardoso, M. Jorge, Cheplygina, Veronika, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kofler, Florian, Kopp-Schneider, Annette, 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, Rajpoot, Nasir, Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, and Jäger, Paul F.

Published
2024

8. Understanding metric-related pitfalls in image analysis validation

Author

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, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, 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., 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., and Maier-Hein, Lena

Abstract

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although 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 multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Published
2024
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9. Metrics reloaded: Recommendations for image analysis validation

Author

Maier-Hein, Lena, Reinke, Annika, Godau, Patrick, Tizabi, Minu D., Büttner, Florian, Christodoulou, Evangelia, Glocker, Ben, Isensee, Fabian, Kleesiek, Jens, Kozubek, Michal, Reyes, Mauricio, Riegler, Michael A., Wiesenfarth, Manuel, Kavur, A. Emre, Sudre, Carole H., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Rädsch, A. Tim, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Cardoso, M. Jorge, Cheplygina, Veronika, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kofler, Florian, Kopp-Schneider, Annette, 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, Rajpoot, Nasir, Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, and Jäger, Paul F.

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases., Shared first authors: Lena Maier-Hein, Annika Reinke

Published
2022

10. Common Limitations of Image Processing Metrics:A Picture Story

Author

Reinke, Annika, Tizabi, Minu D., Sudre, Carole H., Eisenmann, Matthias, Rädsch, Tim, Baumgartner, Michael, Acion, Laura, Antonelli, Michela, Bakas, Spyridon, Bankhead, Peter, Benis, Arriel, Cardoso, M. Jorge, Cheplygina, Veronika, Christodoulou, Evangelia, Cimini, Beth, Collins, Gary S., Farahani, Keyvan, Ginneken, Bram van, Glocker, Ben, Godau, Patrick, Hamprecht, Fred, Hashimoto, Daniel A., Heckmann-Nötzel, Doreen, Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Karargyris, Alexandros, Karthikesalingam, Alan, Kainz, Bernhard, Kavur, Emre, Kenngott, Hannes, Kleesiek, Jens, Kooi, Thijs, 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, Moher, David, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Noyan, M. Alican, Petersen, Jens, Polat, Gorkem, Rajpoot, Nasir, Reyes, Mauricio, Rieke, Nicola, Riegler, Michael, Rivaz, Hassan, Saez-Rodriguez, Julio, Gutierrez, Clarisa Sanchez, Schroeter, Julien, Saha, Anindo, Shetty, Shravya, Smeden, Maarten van, Stieltjes, Bram, Summers, Ronald M., Taha, Abdel A., Tsaftaris, Sotirios A., Calster, Ben Van, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Kopp-Schneider, Annette, Jäger, Paul, and Maier-Hein, Lena

Abstract

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.

Published
2021
Full Text
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12. Understanding metric-related pitfalls in image analysis validation.

Author

Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, Rädsch T, Sudre CH, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko M, Buettner F, Cardoso MJ, Cheplygina V, Chen J, Christodoulou E, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Glocker B, Godau P, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Isensee F, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kenngott H, Kleesiek J, Kofler F, Kooi T, Kopp-Schneider A, Kozubek M, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rafelski SM, Rajpoot N, Reyes M, Riegler MA, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Calster BV, Varoquaux G, Wiesenfarth M, Yaniv ZR, Jäger PF, and Maier-Hein L

Abstract

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.

Published
2024

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