TY - JOUR
T1 - A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets
AU - Peng, Tao
AU - Gu, Yidong
AU - Zhang, Ji
AU - Dong, Yan
AU - Di, Gongye
AU - Wang, Wenjie
AU - Zhao, Jing
AU - Cai, Jing
N1 - Publisher Copyright:
© 2023, The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
PY - 2023
Y1 - 2023
N2 - 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.
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.
KW - Distributed-evolution learning network
KW - Multi-organ
KW - Principal curve
KW - SELU-based explainable mathematical model
KW - Ultrasound segmentation
UR - http://www.scopus.com/inward/record.url?scp=85160310642&partnerID=8YFLogxK
U2 - 10.1007/s10278-023-00839-4
DO - 10.1007/s10278-023-00839-4
M3 - Journal article
AN - SCOPUS:85160310642
SN - 0897-1889
JO - Journal of Digital Imaging
JF - Journal of Digital Imaging
ER -