Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture

[1] Liu T(2019)Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks Med Image Anal 58 101555-520

[2] Guo Q(2019)Parametrical modelling for texture characterization—a novel approach applied to ultrasound thyroid segmentation PLoS ONE 14 e0211215-64074

[3] Lian C(2012)Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan systems Ultrasonics 52 508-63496

[4] Ren X(2020)Thyroid nodule ultrasound image classification through hybrid feature cropping network IEEE Access 8 64064-101828

[5] Liang S(2020)Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning IEEE Access 8 63482-193

[6] Yu J(2019)Integrating five feature types extracted from ultrasonograms to improve the prediction of thyroid papillary carcinoma IEEE Access 7 101820-128439

[7] Niu L(2013)Segmentation of B-type ultrasound image of thyroid tumor based on improved normalized cut Comput Eng Appl 49 190-121

[8] Sun W(2020)High accuracy thyroid tumor image recognition based on hybrid multiple models optimization IEEE Access 8 128426-8937

[9] Shen D(2019)Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images Biocybern Biomed Eng 39 100-1691

[10] Illanes A(2022)Despeckling filters applied to thyroid ultrasound images: a comparative analysis Multimed Tools Appl 81 8905-1357

[1] Liu T(2019)Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks Med Image Anal 58 101555-520

[2] Guo Q(2019)Parametrical modelling for texture characterization—a novel approach applied to ultrasound thyroid segmentation PLoS ONE 14 e0211215-64074

[3] Lian C(2012)Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan systems Ultrasonics 52 508-63496

[4] Ren X(2020)Thyroid nodule ultrasound image classification through hybrid feature cropping network IEEE Access 8 64064-101828

[5] Liang S(2020)Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning IEEE Access 8 63482-193

[6] Yu J(2019)Integrating five feature types extracted from ultrasonograms to improve the prediction of thyroid papillary carcinoma IEEE Access 7 101820-128439

[7] Niu L(2013)Segmentation of B-type ultrasound image of thyroid tumor based on improved normalized cut Comput Eng Appl 49 190-121

[8] Sun W(2020)High accuracy thyroid tumor image recognition based on hybrid multiple models optimization IEEE Access 8 128426-8937

[9] Shen D(2019)Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images Biocybern Biomed Eng 39 100-1691

[10] Illanes A(2022)Despeckling filters applied to thyroid ultrasound images: a comparative analysis Multimed Tools Appl 81 8905-1357