Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network
An ultrasonic thyroid nodule segmentation model based on conditional segmentation adversarial network (cSegAN) was proposed to achieve more accurate segmentation of thyroid nodules. The model is composed of two parts: a segmenter network and a discriminator network. The segmenter network design uses...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Journal of Taiyuan University of Technology
2023-03-01
|
| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2053.html |
| _version_ | 1797208317189160960 |
|---|---|
| author | Junxia WU Yan QIANG Mengnan WANG Yijia WU |
| author_facet | Junxia WU Yan QIANG Mengnan WANG Yijia WU |
| author_sort | Junxia WU |
| collection | DOAJ |
| description | An ultrasonic thyroid nodule segmentation model based on conditional segmentation adversarial network (cSegAN) was proposed to achieve more accurate segmentation of thyroid nodules. The model is composed of two parts: a segmenter network and a discriminator network. The segmenter network design uses a multi-expansion rate convolution block to accurately locate the nodule area, learn to extract the depth and shallow feature information of the nodule, and obtain binary mask of nodule area; the discriminator network compares the gap between the segmentation result and the gold standard to evaluate the segmentation result. Through multiple adversarial training, the experimental results show that the pixel accuracy of the proposed model reaches 0.953 1, which is better than other segmentation models, and can achieve ultrasonic thyroid nodule segmentation more accurately. |
| first_indexed | 2024-04-24T09:36:53Z |
| format | Article |
| id | doaj.art-cc22e3b377264398a27bf688b9698e18 |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:53Z |
| publishDate | 2023-03-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-cc22e3b377264398a27bf688b9698e182024-04-15T09:16:35ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322023-03-0154239239810.16355/j.cnki.issn1007-9432tyut.2023.02.0201007-9432(2023)02-0392-07Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial NetworkJunxia WU0Yan QIANG1Mengnan WANG2Yijia WU3College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaAn ultrasonic thyroid nodule segmentation model based on conditional segmentation adversarial network (cSegAN) was proposed to achieve more accurate segmentation of thyroid nodules. The model is composed of two parts: a segmenter network and a discriminator network. The segmenter network design uses a multi-expansion rate convolution block to accurately locate the nodule area, learn to extract the depth and shallow feature information of the nodule, and obtain binary mask of nodule area; the discriminator network compares the gap between the segmentation result and the gold standard to evaluate the segmentation result. Through multiple adversarial training, the experimental results show that the pixel accuracy of the proposed model reaches 0.953 1, which is better than other segmentation models, and can achieve ultrasonic thyroid nodule segmentation more accurately.https://tyutjournal.tyut.edu.cn/englishpaper/show-2053.htmlthyroid nodule segmentationconvolutional neural networksegmentation adversarial networkultrasound imagesadversarial training |
| spellingShingle | Junxia WU Yan QIANG Mengnan WANG Yijia WU Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network Taiyuan Ligong Daxue xuebao thyroid nodule segmentation convolutional neural network segmentation adversarial network ultrasound images adversarial training |
| title | Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network |
| title_full | Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network |
| title_fullStr | Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network |
| title_full_unstemmed | Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network |
| title_short | Ultrasonic Thyroid Nodule Segmentation Based on Segmentation Adversarial Network |
| title_sort | ultrasonic thyroid nodule segmentation based on segmentation adversarial network |
| topic | thyroid nodule segmentation convolutional neural network segmentation adversarial network ultrasound images adversarial training |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2053.html |
| work_keys_str_mv | AT junxiawu ultrasonicthyroidnodulesegmentationbasedonsegmentationadversarialnetwork AT yanqiang ultrasonicthyroidnodulesegmentationbasedonsegmentationadversarialnetwork AT mengnanwang ultrasonicthyroidnodulesegmentationbasedonsegmentationadversarialnetwork AT yijiawu ultrasonicthyroidnodulesegmentationbasedonsegmentationadversarialnetwork |