A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.
Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal cur...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1515 - 1533 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808831&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808831 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808831 163901492 169808831 169808831 10.1007/s10278-023-00839-4 169808831 ppf: 1515 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets. aug: au: Peng, Tao Gu, Yidong Zhang, Ji Dong, Yan DI, Gongye Wang, Wenjie Zhao, Jing Cai, Jing affil: School of Future Science and Engineering, Soochow University, Suzhou, China sug: subj: Kidney Ultrasonography Prostate Ultrasonography Image Processing, Computer Assisted Algorithms Models, Statistical Artifacts Learning Methods Magnetic Resonance Imaging Tomography, X-Ray Computed Digital Imaging Methods ab: Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal curve–based projection stage into an improved neutrosophic mean shift–based algorithm to acquire the data sequence, for which we utilized a limited amount of prior seed point information as the approximate initialization. Second, a distribution-based evolution technique was designed to aid in the identification of a suitable learning network. Then, utilizing the data sequence as the input of the learning network, we achieved the optimal learning network after learning network training. Finally, a scaled exponential linear unit–based interpretable mathematical model of the organ boundary was expressed via the parameters of a fraction-based learning network. The experimental outcomes indicated that our algorithm 1) achieved more satisfactory segmentation outcomes than state-of-the-art algorithms, with a Dice score coefficient value of 96.68 ± 2.2%, a Jaccard index value of 95.65 ± 2.16%, and an accuracy of 96.54 ± 1.82% and 2) discovered missing or blurry areas. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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