RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images
| Title: | RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images |
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| Authors: | Zhaotong Guo, Pinle Qin, Jianchao Zeng, Rui Chai, Zhifang Wu, Jinjing Zhang, Jia Qin, Zanxia Jin, Pengcheng Zhao, Yixiong Wang |
| Source: | IET Image Processing, Vol 18, Iss 10, Pp 2715-2731 (2024) |
| Publisher Information: | Wiley, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Computer software |
| Subject Terms: | biomedical imaging, computerised tomography, image segmentation, level set segmentation, medical image processing, single‐photon emission computed tomography/computed tomography (SPECT/CT), Photography, TR1-1050, Computer software, QA76.75-76.765 |
| More Details: | Abstract Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1751-9667 1751-9659 33088454 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.13130 |
| Access URL: | https://doaj.org/article/34babfd5a9d84464ac3308845469c3d6 |
| Accession Number: | edsdoj.34babfd5a9d84464ac3308845469c3d6 |
| Database: | Directory of Open Access Journals |
| ISSN: | 17519667 17519659 33088454 |
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| DOI: | 10.1049/ipr2.13130 |
| Published in: | IET Image Processing |
| Language: | English |