Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2501.15588 (eess)
[Submitted on 26 Jan 2025]
Title:Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge
Authors:Gongning Luo,Mingwang Xu,Hongyu Chen,Xinjie Liang,Xing Tao,Dong Ni,Hyunsu Jeong,Chulhong Kim,Raphael Stock,Michael Baumgartner,Yannick Kirchhoff,Maximilian Rokuss,Klaus Maier-Hein,Zhikai Yang,Tianyu Fan,Nicolas Boutry,Dmitry Tereshchenko,Arthur Moine,Maximilien Charmetant,Jan Sauer,Hao Du,Xiang-Hui Bai,Vipul Pai Raikar,Ricardo Montoya-del-Angel,Robert Marti,Miguel Luna,Dongmin Lee,Abdul Qayyum,Moona Mazher,Qihui Guo,Changyan Wang,Navchetan Awasthi,Qiaochu Zhao,Wei Wang,Kuanquan Wang,Qiucheng Wang,Suyu Dong
View a PDF of the paper titled Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge, by Gongning Luo and 36 other authors
View PDFHTML (experimental)Abstract:Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform atthis https URL to benchmark and inspire future developments in algorithmic research.
| Subjects: | Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2501.15588 [eess.IV] |
| (orarXiv:2501.15588v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2501.15588 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge, by Gongning Luo and 36 other authors
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