FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts
The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/9/950 |
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| author | Shurui Bai Zhuo Deng Jingyan Yang Zheng Gong Weihao Gao Lei Shao Fang Li Wenbin Wei Lan Ma |
| author_facet | Shurui Bai Zhuo Deng Jingyan Yang Zheng Gong Weihao Gao Lei Shao Fang Li Wenbin Wei Lan Ma |
| author_sort | Shurui Bai |
| collection | DOAJ |
| description | The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet’s superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation. |
| format | Article |
| id | doaj-art-e6032b636aff4740ae44573f11be3d09 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-e6032b636aff4740ae44573f11be3d092025-08-20T01:56:10ZengMDPI AGBioengineering2306-53542024-09-0111995010.3390/bioengineering11090950FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and PromptsShurui Bai0Zhuo Deng1Jingyan Yang2Zheng Gong3Weihao Gao4Lei Shao5Fang Li6Wenbin Wei7Lan Ma8Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaBeijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaBeijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaBeijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaThe segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet’s superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation.https://www.mdpi.com/2306-5354/11/9/950fundus tumorsegmentationdeep learning |
| spellingShingle | Shurui Bai Zhuo Deng Jingyan Yang Zheng Gong Weihao Gao Lei Shao Fang Li Wenbin Wei Lan Ma FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts Bioengineering fundus tumor segmentation deep learning |
| title | FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts |
| title_full | FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts |
| title_fullStr | FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts |
| title_full_unstemmed | FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts |
| title_short | FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts |
| title_sort | ftsnet fundus tumor segmentation network on multiple scales guided by classification results and prompts |
| topic | fundus tumor segmentation deep learning |
| url | https://www.mdpi.com/2306-5354/11/9/950 |
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