DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray

Ibrahim Ethem Hamamci; Sezgin Er; Omer Faruk Durugol; Gulsade Rabia Cakmak; Ezequiel de la Rosa; Enis Simsar; Atif Emre Yuksel; Sadullah Gultekin; Serife Damla Ozdemir; Kaiyuan Yang; Mehmet Berke Isler; Mustafa Salih Gucez; Shenxiao Mei; Chenglong Ma; Feihong Shen; Kaidi Shen; Huikai Wu; Han Wu; Lanzhuju Mei; Zhiming Cui; Niels van Nistelrooij; Khalid El Ghoul; Steven Kempers; Tong Xi; Shankeeth Vinayahalingam; Kyoungyeon Choi; Jaewon Shin; Eunyi Lyou; Lanshan He; Yusheng Liu; Lisheng Wang; Tudor Dascalu; Shaqayeq Ramezanzade; Azam Bakhshandeh; Lars Bjørndal; Bulat Ibragimov; Hongwei Bran Li; Sarthak Pati; Bernd Stadlinger; Albert Mehl; Mehmet Kemal Ozdemir; Mustafa Gundogar; Bjoern Menze

arXiv · 2305.19112

DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray

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Abstract

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

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Ibrahim Ethem Hamamci, Sezgin Er, Omer Faruk Durugol, Gulsade Rabia Cakmak, Ezequiel de la Rosa, Enis Simsar, Atif Emre Yuksel, Sadullah Gultekin, Serife Damla Ozdemir, Kaiyuan Yang, Mehmet Berke Isler, Mustafa Salih Gucez, Shenxiao Mei, Chenglong Ma, Feihong Shen, Kaidi Shen, Huikai Wu, Han Wu, Lanzhuju Mei, Zhiming Cui, Niels van Nistelrooij, Khalid El Ghoul, Steven Kempers, Tong Xi, Shankeeth Vinayahalingam, Kyoungyeon Choi, Jaewon Shin, Eunyi Lyou, Lanshan He, Yusheng Liu, Lisheng Wang, Tudor Dascalu, Shaqayeq Ramezanzade, Azam Bakhshandeh, Lars Bjørndal, Bulat Ibragimov, Hongwei Bran Li, Sarthak Pati, Bernd Stadlinger, Albert Mehl, Mehmet Kemal Ozdemir, Mustafa Gundogar, Bjoern Menze. 2023-05-30. DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray. https://arxiv.org/abs/2305.19112

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