- The TN-SCUI2020 is in conjunction with MICCAI2020
- The challenge is affiliated with the MICCAI ASMUS Workshop
- The challenge has been opened. The participants can join the challenge.
- The training set has been released here.
- The test set has been released here.
- Time for submission is starting....
- The submission is end. The result will be announced soon.
The thyroid gland is a butterfly-shaped endocrine gland that is normally located in the lower front of the neck. It secretes indispensable hormones that are necessary for all the cells in your body to work normally . The term thyroid nodule refers to an abnormal growth of thyroid cells that forms a lump within the thyroid gland .Recently, many computer-aided diagnosis (CAD) systems have been used to alleviate this problem. However, it is usually difficult to evaluate each of their efficacy as no benchmark was available. Our challenge, named TN-SCUI2020, aims to provide such a platform to validate all of the state-of-the-art methods and exchange for new ideas.
Statistical studies show that the incidence of this disease increases with age, extending to more than 50 % of the world's population. Until recently, thyroid cancer was the most quickly increasing cancer diagnosis in the United States. It is the most common cancer in women 20 to 34 . Although the vast majority of thyroid nodules are benign (noncancerous), a small proportion of thyroid nodules contains thyroid cancer. In order to diagnose and treat thyroid cancer at the earliest stage, it is desired to characterize the nodule accurately.
Thyroid ultrasound is a key tool for thyroid nodule evaluation. It is non-invasive, real-time and radiation-free. However, it is difficult to interpret ultrasound images and recognize the subtle difference between malignant and benign nodules. The diagnosis process is thus time-consuming and heavily depends on the knowledge and the experience of clinicians.
The main topic of this TN-SCUI2020 challenge is finding automatic algorithms to accurately classify the thyroid nodules in ultrasound images. It will provide the biggest public dataset of thyroid nodule with over 4500 patient cases from different ages, genders, and were collected using different ultrasound machines. Each ultrasound image is provided with its ground truth class (benign or maglinant) and a detailed delineation of the nodule.. The dataset comes from the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound (CAAU) which was initiated by Dr. Jiaqiao Zhou, Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University. This challenge will provide a unique opportunity for participants from different backgrounds (e.g. academia, industry, and government, etc.) to compare their algorithms in an impartial way.
According to the MICCAI proposal (March 19,2020), Participation policies,Point f:
The publish right of this dataset is limited to the purpose of this challenge ONLY due to the ethical approval was obtained. No publication rights will be given on the dataset images apart from thischallenge. The participants will not be able to use the dataset images in any other publication or study.
We apologize for any inconvience this might cause.
For more details on the TN-SCUI2020, the participants can refer to the MICCAI proposal at
 Horvath, Eleonora, et al. "An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management." The Journal of Clinical Endocrinology & Metabolism 94.5 (2009): 1748-1751.
 Haugen, Bryan R., et al. "2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer." Thyroid 26.1 (2016): 1-133.
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