ArticleJournal of ultrasound2023
Objective assessment of segmentation models for thyroid ultrasound images.
Article in Journal of ultrasound, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images.Journal of ultrasound · 2024Pooled it
- DLoG - automatic disc based laplacian of gaussian operator for carotid artery boundary extraction in ultrasound images.Scientific reports · 2026Article
- Deep learning based on ultrasound for differential diagnosis of pancreatic serous cystic neoplasm and mucinous cystic neoplasm.BMC cancer · 2025Article
- From pretraining to privacy: federated ultrasound foundation model with self-supervised learning.NPJ digital medicine · 2025Article
- Malignancy prediction for calcified thyroid nodules using deep learning based on ultrasound dynamic videos.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios.BMC endocrine disorders · 2025Article
- Optimized YOLOv8 for enhanced breast tumor segmentation in ultrasound imaging.Discover oncology · 2025Article
- A weakly-supervised follicle segmentation method in ultrasound images.Scientific reports · 2025Article
- A deep learning based ultrasound diagnostic tool driven by 3D visualization of thyroid nodules.NPJ digital medicine · 2025Article
- ThyroNet-X4 genesis: an advanced deep learning model for auxiliary diagnosis of thyroid nodules' malignancy.Scientific reports · 2025Article
- Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images.Scientific reports · 2025Article
- Identification of lesion location and discrimination between benign and malignant findings in thyroid ultrasound imaging.Scientific reports · 2024Article
- An ultrasound image segmentation method for thyroid nodules based on dual-path attention mechanism-enhanced UNet+.BMC medical imaging · 2024Article
- Deep learning based binary classification of diabetic retinopathy images using transfer learning approach.Journal of diabetes and metabolic disorders · 2024Article
- Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour.Scientific reports · 2024Article
- Boundary-aware convolutional attention network for liver segmentation in ultrasound images.Scientific reports · 2024Article
- Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI.BMC medical imaging · 2024Article
- Identifying diversity of patient anatomy through automated image analysis of clinical ultrasounds.Journal of ultrasound · 2024Article
- Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach.BMC medical imaging · 2024Article
- Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.BMC medical informatics and decision making · 2024Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ultrasonography B-mode scans, a multi-output CNN-based semantic segmentation is used to separate thyroid nodules' cystic & solid components. Semantic segmentation is an automatic technique that labels the ultrasound (US) pixels with an appropriate class or pixel category, i.e., belongs to a lesion or background. In the present study, encoder-decoder-based semantic segmentation models i.e. SegNet using VGG16, UNet, and Hybrid-UNet implemented for segmentation of thyroid US images. For this work, 820 thyroid US images are collected from the DDTI and ultrasoundcases.info (USC) datasets. These segmentation models were trained using a transfer learning approach with original and despeckled thyroid US images. The performance of segmentation models is evaluated by analyzing the overlap region between the true contour lesion marked by the radiologist and the lesion retrieved by the segmentation model. The mean intersection of union (mIoU), mean dice coefficient (mDC) metrics, TPR, TNR, FPR, and FNR metrics are used to measure performance. Based on the exhaustive experiments and performance evaluation parameters it is observed that the proposed Hybrid-UNet segmentation model segments thyroid nodules and cystic components effectively.
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