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Peng T, Gu Y, Zhang J, Dong Y, DI G, Wang W, Zhao J, Cai J. A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets. J Digit Imaging 2023; 36:1515-1532. [PMID: 37231289 PMCID: PMC10406792 DOI: 10.1007/s10278-023-00839-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
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.
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Affiliation(s)
- Tao Peng
- School of Future Science and Engineering, Soochow University, Suzhou, China
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX USA
| | - Yidong Gu
- School of Future Science and Engineering, Soochow University, Suzhou, China
- Department of Medical Ultrasound, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu China
| | - Ji Zhang
- Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, China
| | - Yan Dong
- Department of Ultrasonography, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Gongye DI
- Department of Ultrasonic, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, China
| | - Wenjie Wang
- Department of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, Jiangsu China
| | - Jing Zhao
- Department of Ultrasound, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Jing Cai
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
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