Name
Title | ||
|---|---|---|
Thyroid Nodule Segmentation and Classification Using Deep Convolutional Neural Network and Rule-based Classifiers |
Abstract | ||
|---|---|---|
Thyroid cancer has a high prevalence all over the world. Accurate thyroid nodule diagnosis can lead to effective treatment and decrease the mortality rate. Ultrasound imaging is a safe, portable, and inexpensive tool for thyroid nodule monitoring. However, the widespread use of ultrasound has also resulted in over-diagnosis and over-treatment of nodules. There is also large variability in the assessment and characterization of nodules. Thyroid nodule classification requires precise delineation of the nodule boundary which is tedious and time-consuming. Automatic segmentation of nodule boundaries is highly desirable, however, it is challenging due to the wide range of nodule appearances, shapes, and sizes. In this study, we propose an end-to-end pipeline for nodule segmentation and classification. A residual dilated UNet (resDUnet) model is proposed for nodule segmentation. The output of resDUnet is fed to two rule-based classifiers to categorize the composition and echogenicity of the segmented nodule. We evaluate our segmentation method on a large dataset of 352 ultrasound images reviewed by a certified radiologist. When compared with ground-truth, resDUnet gives a higher Dice score than the standard UNet (82% vs. 81%). Our method requires minimal user interaction and it is robust to reasonable variations in the user-specified region-of-interest. We expect the proposed method to reduce variability in thyroid nodule assessment which results in more efficient and cost-effective monitoring of thyroid cancer. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EMBC46164.2021.9629557 | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) |
DocType | Volume | ISSN |
Conference | 2021 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atefeh Shahroudnejad | 1 | 0 | 0.34 |
| Roberto Vega | 2 | 0 | 0.34 |
| Amir Forouzandeh | 3 | 0 | 0.34 |
| Sharanya Balachandran | 4 | 0 | 0.34 |
| Jacob Jaremko | 5 | 0 | 0.34 |
| Michelle Noga | 6 | 0 | 0.34 |
| Abhilash Rakkunedeth Hareendranathan | 7 | 0 | 2.70 |
| Jeevesh Kapur | 8 | 0 | 1.35 |
| Kumaradeven Punithakumar | 9 | 0 | 0.68 |