Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data
We present an automatic kidney segmentation method using ultrasound images. This method employs a coarse-to-fine approach to tackle the challenge of unclear and fuzzy boundaries. Four key innovations are introduced to enhance the segmentation process’s accuracy and efficiency. First, an automatic de...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020008 |
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| author | Tao Peng Yiwen Ruan Yidong Gu Jiang Huang Caiyin Tang Jing Cai |
| author_facet | Tao Peng Yiwen Ruan Yidong Gu Jiang Huang Caiyin Tang Jing Cai |
| author_sort | Tao Peng |
| collection | DOAJ |
| description | We present an automatic kidney segmentation method using ultrasound images. This method employs a coarse-to-fine approach to tackle the challenge of unclear and fuzzy boundaries. Four key innovations are introduced to enhance the segmentation process’s accuracy and efficiency. First, an automatic deep fusion training network serves as a coarse segmentation strategy. Second, we propose an explainable mathematical mapping formula to better represent the kidney contour. Third, by utilizing the characteristics of the principal curve, a neural network automatically refines curve shapes, thus reducing model errors. Finally, we employ an intelligent searching polyline segment method for automatic kidney contour segmentation. The results show that our method achieves high accuracy and stability in segmenting kidney ultrasound images. This work’s contributions include the deep fusion training network, intelligent searching polyline segment method, and explainable mathematical mapping formula, which are applicable to other medical image segmentation tasks. Additionally, this approach uses a mean-shift clustering model, supplanting standard projection and vertex optimization steps. |
| format | Article |
| id | doaj-art-4b72ec19c6ec4b37a2bfb36dcb06b7ea |
| institution | OA Journals |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-4b72ec19c6ec4b37a2bfb36dcb06b7ea2025-08-20T02:17:49ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-12-01741321133210.26599/BDMA.2024.9020008Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound DataTao Peng0Yiwen Ruan1Yidong Gu2Jiang Huang3Caiyin Tang4Jing Cai5School of Future Science and Engineering, Soochow University, Suzhou 215222, ChinaSchool of Future Science and Engineering, Soochow University, Suzhou 215222, ChinaDepartment of Medical Ultrasound, Suzhou Municipal Hospital, Suzhou 215006, ChinaDepartment of Ophthalmology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, ChinaDepartment of Radiology, Taizhou People’s Hospital Affiliated to Nanjing Medical University, Taizhou 318020, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaWe present an automatic kidney segmentation method using ultrasound images. This method employs a coarse-to-fine approach to tackle the challenge of unclear and fuzzy boundaries. Four key innovations are introduced to enhance the segmentation process’s accuracy and efficiency. First, an automatic deep fusion training network serves as a coarse segmentation strategy. Second, we propose an explainable mathematical mapping formula to better represent the kidney contour. Third, by utilizing the characteristics of the principal curve, a neural network automatically refines curve shapes, thus reducing model errors. Finally, we employ an intelligent searching polyline segment method for automatic kidney contour segmentation. The results show that our method achieves high accuracy and stability in segmenting kidney ultrasound images. This work’s contributions include the deep fusion training network, intelligent searching polyline segment method, and explainable mathematical mapping formula, which are applicable to other medical image segmentation tasks. Additionally, this approach uses a mean-shift clustering model, supplanting standard projection and vertex optimization steps.https://www.sciopen.com/article/10.26599/BDMA.2024.9020008polyline segment techniqueartificial neural networkexplainable mathematical mapping formulaultrasound kidney segmentation |
| spellingShingle | Tao Peng Yiwen Ruan Yidong Gu Jiang Huang Caiyin Tang Jing Cai Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data Big Data Mining and Analytics polyline segment technique artificial neural network explainable mathematical mapping formula ultrasound kidney segmentation |
| title | Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data |
| title_full | Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data |
| title_fullStr | Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data |
| title_full_unstemmed | Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data |
| title_short | Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data |
| title_sort | coarse to fine approach automatic delineation of kidney ultrasound data |
| topic | polyline segment technique artificial neural network explainable mathematical mapping formula ultrasound kidney segmentation |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020008 |
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