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A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.
- Source :
- Journal of Digital Imaging; Aug2023, Vol. 36 Issue 4, p1515-1532, 18p, 3 Color Photographs, 1 Black and White Photograph, 5 Diagrams, 13 Charts, 4 Graphs
- Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08971889
- Volume :
- 36
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Digital Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 169808831
- Full Text :
- https://doi.org/10.1007/s10278-023-00839-4