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# Thyroid Nodule Segmentation and Classification in Ultrasound Images

Jianqiao Zhou; Xiaohong Jia; Dong Ni; Alison Noble; Ruobing Huang; Tao Tan; Manh The Van

### Dublin Core Export

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<dc:creator>Jianqiao Zhou</dc:creator>
<dc:creator>Xiaohong Jia</dc:creator>
<dc:creator>Dong Ni</dc:creator>
<dc:creator>Alison Noble</dc:creator>
<dc:creator>Ruobing Huang</dc:creator>
<dc:creator>Tao Tan</dc:creator>
<dc:creator>Manh The Van</dc:creator>
<dc:date>2020-03-19</dc:date>
<dc:description>This is the challenge design document for the "Thyroid Nodule Segmentation and Classification in Ultrasound Images" Challenge, accepted for MICCAI 2020.

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 the body to work normally [1]. The term thyroid nodule refers to an abnormal growth of thyroid cells that forms a lump within the thyroid gland [2]. Statistical studies showed 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 [3]. 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.

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 so far. Our challenge, named TNSCUI2020, aims to provide such a platform to validate all of the state-of-the-art methods and exchange for new ideas.

The main topic of this TN-SCUI2020 challenge is finding automatic algorithms to accurately segment and classify the thyroid nodules in ultrasound images. It will provide the biggest public dataset of thyroid nodule with over 4,500 patient cases from different ages, genders, and were collected using different ultrasound machines. Each ultrasound image is provided with its annotated class (benign or malignant) and a detailed delineation of the nodule. The dataset comes from the Chinese Medical Ultrasound Artificial Intelligence Alliance (CMUAIA) 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.

References

[1] https://www.btf-thyroid.org/what-is-thyroid-disorder.
[3] https://www.cancer.net/cancer-types/thyroid-cancer/statistics.</dc:description>
<dc:identifier>https://zenodo.org/record/3715942</dc:identifier>
<dc:identifier>10.5281/zenodo.3715942</dc:identifier>
<dc:identifier>oai:zenodo.org:3715942</dc:identifier>
<dc:relation>doi:10.5281/zenodo.3715941</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:subject>MICCAI Challenges</dc:subject>
<dc:subject>Biomedical Challenges</dc:subject>
<dc:subject>MICCAI</dc:subject>
<dc:subject>Thyroid Nodule</dc:subject>
<dc:subject>Ultrasound Images</dc:subject>
<dc:subject>Segmentation</dc:subject>
<dc:subject>Classification</dc:subject>
<dc:title>Thyroid Nodule Segmentation and Classification in Ultrasound Images</dc:title>
<dc:type>info:eu-repo/semantics/other</dc:type>
<dc:type>other</dc:type>
</oai_dc:dc>

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