{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:37:17Z","timestamp":1780634237229,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04676-4","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:02:25Z","timestamp":1685592145000},"page":"21390-21406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automatic coarse-to-refinement-based ultrasound prostate segmentation using optimal polyline segment tracking method and deep learning"],"prefix":"10.1007","volume":"53","author":[{"given":"Tao","family":"Peng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daqiang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyin","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuntian","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"4676_CR1","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1038\/s41585-019-0193-3","volume":"16","author":"SL Goldenberg","year":"2019","unstructured":"Goldenberg SL, Nir G, Salcudean SE (2019) A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol 16:391\u2013403","journal-title":"Nat Rev Urol"},{"key":"4676_CR2","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1109\/TMI.2021.3139999","volume":"41","author":"X Xu","year":"2022","unstructured":"Xu X, Sanford T, Turkbey B et al (2022) Shadow-consistent semi-supervised learning for prostate ultrasound segmentation. IEEE Trans Med Imaging 41:1331\u20131345","journal-title":"IEEE Trans Med Imaging"},{"key":"4676_CR3","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s10462-022-10179-4","volume":"56","author":"J Jiang","year":"2023","unstructured":"Jiang J, Guo Y, Bi Z et al (2023) Segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms. Artif Intell Rev 56:615\u2013651","journal-title":"Artif Intell Rev"},{"key":"4676_CR4","volume":"185","author":"G Chen","year":"2021","unstructured":"Chen G, Dai Y, Li R et al (2021) SDFNet: automatic segmentation of kidney ultrasound images using multi-scale low-level structural feature. Expert Syst Appl 185:115619","journal-title":"Expert Syst Appl"},{"key":"4676_CR5","doi-asserted-by":"crossref","unstructured":"Peng T, Zhao J, Wang J (2021) Interpretable mathematical model-guided ultrasound prostate contour extraction using data mining techniques. In: IEEE 15th International Conference on Bioinformatics and Biomedicine (BIBM), pp 1037\u20131044","DOI":"10.1109\/BIBM52615.2021.9669419"},{"key":"4676_CR6","volume":"71","author":"K He","year":"2021","unstructured":"He K, Lian C, Adeli E et al (2021) MetricUNet: synergistic image- and voxel-level learning for precise prostate segmentation via online sampling. Med Image Anal 71:102039","journal-title":"Med Image Anal"},{"key":"4676_CR7","unstructured":"Chen G, Li L, Dai Y et al (2022) AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images. IEEE Trans Med Imaging:1\u20131"},{"key":"4676_CR8","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.future.2020.08.015","volume":"114","author":"Z Liu","year":"2021","unstructured":"Liu Z, Yang C, Huang J et al (2021) Deep learning framework based on integration of S-mask R-CNN and inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Futur Gener Comput Syst 114:358\u2013367","journal-title":"Futur Gener Comput Syst"},{"key":"4676_CR9","volume":"117","author":"G Chen","year":"2023","unstructured":"Chen G, Dai Y, Zhang J (2023) RRCNet: refinement residual convolutional network for breast ultrasound images segmentation. Eng Appl Artif Intell 117:105601","journal-title":"Eng Appl Artif Intell"},{"key":"4676_CR10","volume":"198","author":"T Peng","year":"2022","unstructured":"Peng T, Gu Y, Ye Z et al (2022) A-LugSeg: automatic and explainability-guided multi-site lung detection in chest X-ray images. Expert Syst Appl 198:116873","journal-title":"Expert Syst Appl"},{"key":"4676_CR11","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac5d74","volume":"67","author":"T Peng","year":"2022","unstructured":"Peng T, Wang C, Zhang Y, Wang J (2022) H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method. Phys Med Biol 67:075006","journal-title":"Phys Med Biol"},{"key":"4676_CR12","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1002\/mp.12898","volume":"45","author":"M Shahedi","year":"2018","unstructured":"Shahedi M, Halicek M, Guo R et al (2018) A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling. Med Phys 45:2527\u20132541","journal-title":"Med Phys"},{"key":"4676_CR13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.media.2018.05.010","volume":"48","author":"EMA Anas","year":"2018","unstructured":"Anas EMA, Mousavi P, Abolmaesumi P (2018) A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy. Med Image Anal 48:107\u2013116","journal-title":"Med Image Anal"},{"key":"4676_CR14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec H, Dolz J, Tang M et al (2019) Constrained-CNN losses for weakly supervised segmentation. Med Image Anal 54:88\u201399","journal-title":"Med Image Anal"},{"key":"4676_CR15","doi-asserted-by":"crossref","unstructured":"Zhang T-T, Shu H, Lam K-Y et al (2023) Feature decomposition and enhancement for unsupervised medical ultrasound image denoising and instance segmentation. Appl Intell\u00a053:9548\u20139561","DOI":"10.1007\/s10489-022-03857-x"},{"key":"4676_CR16","volume":"184","author":"H Bi","year":"2020","unstructured":"Bi H, Jiang Y, Tang H et al (2020) Fast and accurate segmentation method of active shape model with Rayleigh mixture model clustering for prostate ultrasound images. Comput Methods Prog Biomed 184:105097","journal-title":"Comput Methods Prog Biomed"},{"key":"4676_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/mp.13891","volume":"47","author":"X Jia","year":"2020","unstructured":"Jia X, Ren L, Cai J (2020) Clinical implementation of AI technologies will require interpretable AI models. Med Phys 47:1\u20134","journal-title":"Med Phys"},{"key":"4676_CR18","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1002\/mp.12831","volume":"45","author":"L Xing","year":"2018","unstructured":"Xing L, Krupinski EA, Cai J (2018) Artificial intelligence will soon change the landscape of medical physics research and practice. Med Phys 45:1791\u20131793","journal-title":"Med Phys"},{"key":"4676_CR19","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac5a93","volume":"67","author":"N Orlando","year":"2022","unstructured":"Orlando N, Gyacskov I, Gillies DJ et al (2022) Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound. Phys Med Biol 67:074002","journal-title":"Phys Med Biol"},{"key":"4676_CR20","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102620","volume":"82","author":"S Vesal","year":"2022","unstructured":"Vesal S, Gayo I, Bhattacharya I et al (2022) Domain generalization for prostate segmentation in transrectal ultrasound images: a multi-center study. Med Image Anal 82:102620","journal-title":"Med Image Anal"},{"key":"4676_CR21","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1002\/mp.14895","volume":"48","author":"Y Lei","year":"2021","unstructured":"Lei Y, Wang T, Roper J et al (2021) Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN. Med Phys 48:3055\u20133064","journal-title":"Med Phys"},{"key":"4676_CR22","doi-asserted-by":"crossref","unstructured":"Liu Q, Dou Q, Heng P-A (2020) Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains. In: International conference on medical image computing and computer-assisted intervention, pp 475\u2013485","DOI":"10.1007\/978-3-030-59713-9_46"},{"key":"4676_CR23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106752","volume":"219","author":"T Peng","year":"2022","unstructured":"Peng T, Wu Y, Qin J et al (2022) H-ProSeg: hybrid ultrasound prostate segmentation based on explainability-guided mathematical model. Comput Methods Prog Biomed 219:106752","journal-title":"Comput Methods Prog Biomed"},{"key":"4676_CR24","volume":"78","author":"X Xu","year":"2022","unstructured":"Xu X, Sanford T, Turkbey B et al (2022) Polar transform network for prostate ultrasound segmentation with uncertainty estimation. Med Image Anal 78:102418","journal-title":"Med Image Anal"},{"key":"4676_CR25","unstructured":"Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention U-Net: learning where to look for the pancreas. In: Medical Imaging with Deep Learning (MIDL)"},{"key":"4676_CR26","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241"},{"key":"4676_CR27","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/34.841759","volume":"22","author":"B K\u00e9gl","year":"2000","unstructured":"K\u00e9gl B, Linder T, Zeger K (2000) Learning and design of principal curves. IEEE Trans Pattern Anal Machine Intell 22:281\u2013297","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"4676_CR28","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1007\/s10278-018-0058-y","volume":"31","author":"T Peng","year":"2018","unstructured":"Peng T, Wang Y, Xu TC et al (2018) Detection of lung contour with closed principal curve and machine learning. J Digit Imaging 31:520\u2013533","journal-title":"J Digit Imaging"},{"key":"4676_CR29","doi-asserted-by":"crossref","first-page":"73293","DOI":"10.1109\/ACCESS.2020.2987925","volume":"8","author":"T Peng","year":"2020","unstructured":"Peng T, Xu TC, Wang Y et al (2020) Hybrid automatic lung segmentation on chest CT scans. IEEE Access 8:73293\u201373306","journal-title":"IEEE Access"},{"key":"4676_CR30","doi-asserted-by":"crossref","unstructured":"Peng T, Xu TC, Wang Y, Li F (2020) Deep belief network and closed polygonal line for lung segmentation in chest radiographs. Comput J\u00a065:1107\u20131128","DOI":"10.1093\/comjnl\/bxaa148"},{"key":"4676_CR31","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1007\/s10489-020-01645-z","volume":"50","author":"H Zhou","year":"2020","unstructured":"Zhou H, Zhao H, Zhang Y (2020) Nonlinear system modeling using self-organizing fuzzy neural networks for industrial applications. Appl Intell 50:1657\u20131672","journal-title":"Appl Intell"},{"key":"4676_CR32","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1109\/TCYB.2016.2617301","volume":"47","author":"MZ Ali","year":"2017","unstructured":"Ali MZ, Awad NH, Suganthan PN, Reynolds RG (2017) An adaptive multipopulation differential evolution with dynamic population reduction. IEEE Trans Cybern 47:2768\u20132779","journal-title":"IEEE Trans Cybern"},{"key":"4676_CR33","doi-asserted-by":"crossref","unstructured":"Laredo JLJ, Fernandes C, Merelo JJ, Gagn\u00e9 C (2009) Improving genetic algorithms performance via deterministic population shrinkage. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, Montreal, Canada, p 819","DOI":"10.1145\/1569901.1570014"},{"key":"4676_CR34","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer Vision. Venice, Italy, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"4676_CR35","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2020) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39:1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"4676_CR36","first-page":"61","volume-title":"UTNet: a hybrid transformer architecture for medical image segmentation","author":"Y Gao","year":"2021","unstructured":"Gao Y, Zhou M, Metaxas D (2021) UTNet: a hybrid transformer architecture for medical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 61\u201371"},{"key":"4676_CR37","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1007\/978-3-030-98358-1_14","volume-title":"International conference on multimedia modeling (MMM)","author":"T Peng","year":"2022","unstructured":"Peng T, Tang C, Wang J (2022) Prostate segmentation of ultrasound images based on interpretable-guided mathematical model. In: International conference on multimedia modeling (MMM). Springer, pp 166\u2013177"},{"key":"4676_CR38","unstructured":"Nam H, Kim H-E (2018) Batch-instance normalization for adaptively style-invariant neural networks. In: Advances in neural information processing systems"},{"key":"4676_CR39","unstructured":"Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML workshop on deep learning for audio, speech and language processing"},{"key":"4676_CR40","doi-asserted-by":"crossref","unstructured":"Hara K, Saito D, Shouno H (2015) Analysis of function of rectified linear unit used in deep learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN.2015.7280578"},{"key":"4676_CR41","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/TITS.2008.2006780","volume":"9","author":"J Zhang","year":"2008","unstructured":"Zhang J, Chen D, Kruger U (2008) Adaptive constraint K-segment principal curves for intelligent transportation systems. IEEE Trans Intell Transport Syst 9:666\u2013677","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"4676_CR42","doi-asserted-by":"crossref","unstructured":"Kabir W, Ahmad MO, Swamy MNS (2015) A novel normalization technique for multimodal biometric systems. In: 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, Fort Collins, pp 1\u20134","DOI":"10.1109\/MWSCAS.2015.7282214"},{"key":"4676_CR43","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.eswa.2019.06.035","volume":"136","author":"P Chen","year":"2019","unstructured":"Chen P (2019) Effects of normalization on the entropy-based TOPSIS method. Expert Syst Appl 136:33\u201341","journal-title":"Expert Syst Appl"},{"key":"4676_CR44","doi-asserted-by":"crossref","unstructured":"Storn R (1997) Differential evolution \u2013 a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341","DOI":"10.1023\/A:1008202821328"},{"key":"4676_CR45","doi-asserted-by":"crossref","first-page":"10448","DOI":"10.1007\/s10489-021-02803-7","volume":"52","author":"RP Parouha","year":"2022","unstructured":"Parouha RP, Verma P (2022) A systematic overview of developments in differential evolution and particle swarm optimization with their advanced suggestion. Appl Intell 52:10448\u201310492","journal-title":"Appl Intell"},{"key":"4676_CR46","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.energy.2017.03.094","volume":"127","author":"Y-R Zeng","year":"2017","unstructured":"Zeng Y-R, Zeng Y, Choi B, Wang L (2017) Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy 127:381\u2013396","journal-title":"Energy"},{"key":"4676_CR47","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108890","volume":"131","author":"T Peng","year":"2022","unstructured":"Peng T, Zhao J, Gu Y et al (2022) H-ProMed: ultrasound image segmentation based on the evolutionary neural network and an improved principal curve. Pattern Recogn 131:108890","journal-title":"Pattern Recogn"},{"key":"4676_CR48","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1007\/s11263-022-01619-3","volume":"130","author":"T Peng","year":"2022","unstructured":"Peng T, Tang C, Wu Y, Cai J (2022) H-SegMed: a hybrid method for prostate segmentation in TRUS images via improved closed principal curve and improved enhanced machine learning. Int J Comput Vis 130:1896\u20131919","journal-title":"Int J Comput Vis"},{"key":"4676_CR49","doi-asserted-by":"crossref","unstructured":"Peng T, Gu Y, Wang J (2021) Lung contour detection in chest X-ray images using mask region-based convolutional neural network and adaptive closed polyline searching method. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pp 2839\u20132842","DOI":"10.1109\/EMBC46164.2021.9630012"},{"key":"4676_CR50","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/34.982884","volume":"24","author":"B Kegl","year":"2002","unstructured":"Kegl B, Krzyzak A (2002) Piecewise linear skeletonization using principal curves. IEEE Trans Pattern Anal Machine Intell 24:59\u201374","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"4676_CR51","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/TMI.2019.2935018","volume":"39","author":"Q Zhu","year":"2020","unstructured":"Zhu Q, Du B, Yan P (2020) Boundary-weighted domain adaptive neural network for prostate MR image segmentation. IEEE Trans Med Imaging 39:753\u2013763","journal-title":"IEEE Trans Med Imaging"},{"key":"4676_CR52","doi-asserted-by":"crossref","first-page":"3194","DOI":"10.1002\/mp.13577","volume":"46","author":"Y Lei","year":"2019","unstructured":"Lei Y, Tian S, He X et al (2019) Ultrasound prostate segmentation based on multidirectional deeply supervised V-net. Med Phys 46:3194\u20133206","journal-title":"Med Phys"},{"key":"4676_CR53","doi-asserted-by":"crossref","first-page":"137794","DOI":"10.1109\/ACCESS.2019.2941511","volume":"7","author":"T Peng","year":"2019","unstructured":"Peng T, Wang Y, Xu TC, Chen X (2019) Segmentation of lung in chest radiographs using Hull and closed polygonal line method. IEEE Access 7:137794\u2013137810","journal-title":"IEEE Access"},{"key":"4676_CR54","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/TMI.2020.3035253","volume":"40","author":"R Gu","year":"2021","unstructured":"Gu R, Wang G, Song T et al (2021) CA-net: comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans Med Imaging 40:699\u2013711","journal-title":"IEEE Trans Med Imaging"},{"key":"4676_CR55","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s42256-019-0055-y","volume":"1","author":"L Floridi","year":"2019","unstructured":"Floridi L (2019) Establishing the rules for building trustworthy AI. Nat Mach Intell 1:261\u2013262","journal-title":"Nat Mach Intell"},{"key":"4676_CR56","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/s42256-022-00516-1","volume":"4","author":"W Liang","year":"2022","unstructured":"Liang W, Tadesse GA, Ho D et al (2022) Advances, challenges and opportunities in creating data for trustworthy AI. Nat Mach Intell 4:669\u2013677","journal-title":"Nat Mach Intell"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04676-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04676-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04676-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T11:16:32Z","timestamp":1695122192000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04676-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":56,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4676"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04676-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]},"assertion":[{"value":"26 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"It is the retrospective study, and the clinicians have obtained patients\u2019 agreement before the ultrasound examination, which is an item covered by the medical insurance program. In summary, there is no need for patient consent in our study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare that they have no conflicts of interest to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/competing interests"}}]}}