Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model.
Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast b...
| Published in: | Journal of Digital Imaging Vol. 36; no. 3; pp. 947 - 964 |
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| Main Authors: | , , , , , |
| Format: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473109&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473109 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473109 161638376 164473109 164473109 10.1007/s10278-023-00783-3 164473109 ppf: 947 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model. aug: au: Peng, Tao Wu, Yiyun Zhao, Jing Wang, Caishan Wang, Jin Cai, Jing affil: School of Future Science and Engineering, Soochow University, Suzhou, China sug: subj: Ultrasonography Methods Prostate Radiography Prostatic Neoplasms Radiography Biological Phenomena Models, Theoretical Algorithms Human Male Image Processing, Computer Assisted Methods Signal Processing, Computer Assisted Methods Neural Networks (Computer) Benchmarking Sensitivity and Specificity Male ab: Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics. 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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