DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray
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| Format: | Preprint |
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2023
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| author | Hamamci, Ibrahim Ethem Er, Sezgin Durugol, Omer Faruk Cakmak, Gulsade Rabia de la Rosa, Ezequiel Simsar, Enis Yuksel, Atif Emre Gultekin, Sadullah Ozdemir, Serife Damla Yang, Kaiyuan Isler, Mehmet Berke Gucez, Mustafa Salih Mei, Shenxiao Ma, Chenglong Shen, Feihong Shen, Kaidi Wu, Huikai Wu, Han Mei, Lanzhuju Cui, Zhiming van Nistelrooij, Niels Ghoul, Khalid El Kempers, Steven Xi, Tong Vinayahalingam, Shankeeth Choi, Kyoungyeon Shin, Jaewon Lyou, Eunyi He, Lanshan Liu, Yusheng Wang, Lisheng Dascalu, Tudor Ramezanzade, Shaqayeq Bakhshandeh, Azam Bjørndal, Lars Ibragimov, Bulat Li, Hongwei Bran Pati, Sarthak Stadlinger, Bernd Mehl, Albert Ozdemir, Mehmet Kemal Gundogar, Mustafa Menze, Bjoern |
| author_facet | Hamamci, Ibrahim Ethem Er, Sezgin Durugol, Omer Faruk Cakmak, Gulsade Rabia de la Rosa, Ezequiel Simsar, Enis Yuksel, Atif Emre Gultekin, Sadullah Ozdemir, Serife Damla Yang, Kaiyuan Isler, Mehmet Berke Gucez, Mustafa Salih Mei, Shenxiao Ma, Chenglong Shen, Feihong Shen, Kaidi Wu, Huikai Wu, Han Mei, Lanzhuju Cui, Zhiming van Nistelrooij, Niels Ghoul, Khalid El Kempers, Steven Xi, Tong Vinayahalingam, Shankeeth Choi, Kyoungyeon Shin, Jaewon Lyou, Eunyi He, Lanshan Liu, Yusheng Wang, Lisheng Dascalu, Tudor Ramezanzade, Shaqayeq Bakhshandeh, Azam Bjørndal, Lars Ibragimov, Bulat Li, Hongwei Bran Pati, Sarthak Stadlinger, Bernd Mehl, Albert Ozdemir, Mehmet Kemal Gundogar, Mustafa Menze, Bjoern |
| contents | Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, we organized the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present a comprehensive analysis of the methods and results from the challenge. Our findings reveal that top performers succeeded through diverse, specialized strategies, from segmentation-guided pipelines to highly-engineered single-stage detectors, using advanced Transformer and diffusion models. These strategies significantly outperformed traditional approaches, particularly for the challenging tasks of tooth enumeration and subtle disease classification. By dissecting the architectural choices that drove success, this paper provides key insights for future development of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEX |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_19112 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray Hamamci, Ibrahim Ethem Er, Sezgin Durugol, Omer Faruk Cakmak, Gulsade Rabia de la Rosa, Ezequiel Simsar, Enis Yuksel, Atif Emre Gultekin, Sadullah Ozdemir, Serife Damla Yang, Kaiyuan Isler, Mehmet Berke Gucez, Mustafa Salih Mei, Shenxiao Ma, Chenglong Shen, Feihong Shen, Kaidi Wu, Huikai Wu, Han Mei, Lanzhuju Cui, Zhiming van Nistelrooij, Niels Ghoul, Khalid El Kempers, Steven Xi, Tong Vinayahalingam, Shankeeth Choi, Kyoungyeon Shin, Jaewon Lyou, Eunyi He, Lanshan Liu, Yusheng Wang, Lisheng Dascalu, Tudor Ramezanzade, Shaqayeq Bakhshandeh, Azam Bjørndal, Lars Ibragimov, Bulat Li, Hongwei Bran Pati, Sarthak Stadlinger, Bernd Mehl, Albert Ozdemir, Mehmet Kemal Gundogar, Mustafa Menze, Bjoern Computer Vision and Pattern Recognition Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, we organized the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present a comprehensive analysis of the methods and results from the challenge. Our findings reveal that top performers succeeded through diverse, specialized strategies, from segmentation-guided pipelines to highly-engineered single-stage detectors, using advanced Transformer and diffusion models. These strategies significantly outperformed traditional approaches, particularly for the challenging tasks of tooth enumeration and subtle disease classification. By dissecting the architectural choices that drove success, this paper provides key insights for future development of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEX |
| title | DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2305.19112 |