ArticleInternational journal of computer assisted radiology and surgery2017
Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.
Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 181 citations in OpenAlex.
- Pooled it
- Performance Evaluation of Deep Learning for the Detection and Segmentation of Thyroid Nodules: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Pooled it
- A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis.Scientific reports · 2026Article
- RPFeaNet: Rethinking Deep Progressive Prompt-Guided Feature Interaction Fusion Network for Medical Ultrasound Image Segmentation.Sensors (Basel, Switzerland) · 2026Article
- EvoThy-Net: an evolutionary encoder-decoder network for thyroid nodule segmentation in ultrasound imaging.Scientific reports · 2026Article
- Improving Non-Invasive Prediction of Thyroid Nodule Malignancy: A Machine Learning-Based Clinical Approach.Cancer management and research · 2026Article
- RTS-Net: thyroid nodule segmentation network integrating dual-path attention and graph convolution.Frontiers in medicine · 2026Article
- Deep learning-based ultrasound diagnostic model for follicular thyroid carcinoma.European radiology · 2026Article
- Spatial-Temporal Information Fusion for Thyroid Nodule Segmentation in Dynamic Contrast-Enhanced MRI: A Novel Approach.Journal of imaging informatics in medicine · 2025Article
- A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation.Scientific reports · 2025Article
- Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model.Frontiers in oncology · 2025Article
- Res-ECA-UNet++: an automatic segmentation model for ovarian tumor ultrasound images based on residual networks and channel attention mechanism.Frontiers in medicine · 2025Article
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- The Horizon of Thyroid Imaging Reporting and Data System in the Diagnostic Performance of Thyroid Nodules: Clinical Application and Future Perspectives.TouchREVIEWS in endocrinology · 2024Review
- US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?European radiology · 2024Article
- Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Thyroid Nodule Detection and Region Estimation in Ultrasound Images: A Comparison between Physicians and an Automated Decision Support System Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Objective assessment of segmentation models for thyroid ultrasound images.Journal of ultrasound · 2023Article
- Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images.Medical & biological engineering & computing · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDelineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images.
methodsOur CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset.
resultsThe experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively.
conclusionOur proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.