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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.17690 |
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| _version_ | 1866909923276750848 |
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| author | Zeng, Zixue Zhao, Xiaoyan Cartier, Matthew Yu, Tong Wang, Jing Meng, Xin Sheng, Zhiyu Satarpour, Maryam Cormack, John M Bean, Allison Nussbaum, Ryan Maurer, Maya Landis-Walkenhorst, Emily Kumbhare, Dinesh Kim, Kang Wasan, Ajay Pu, Jiantao |
| author_facet | Zeng, Zixue Zhao, Xiaoyan Cartier, Matthew Yu, Tong Wang, Jing Meng, Xin Sheng, Zhiyu Satarpour, Maryam Cormack, John M Bean, Allison Nussbaum, Ryan Maurer, Maya Landis-Walkenhorst, Emily Kumbhare, Dinesh Kim, Kang Wasan, Ajay Pu, Jiantao |
| contents | We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhancement (SGE) is also developed, where the generator produces images tailored specifically for the segmentation model. Two variants of GRN were also developed, including GRN for sample-efficient learning (GRN-SEL) and GRN for semi-supervised learning (GRN-SSL). GRN's performance was evaluated using a dataset of 69 fully annotated 3D ultrasound scans from 29 subjects. The annotations included six anatomical structures: dermis, superficial fat, superficial fascial membrane (SFM), deep fat, deep fascial membrane (DFM), and muscle. Our results show that GRN-SEL with SGE reduces labeling efforts by up to 70% while achieving a 1.98% improvement in the Dice Similarity Coefficient (DSC) compared to models trained on fully labeled datasets. GRN-SEL alone reduces labeling efforts by 60%, GRN-SSL with SGE decreases labeling requirements by 70%, and GRN-SSL alone by 60%, all while maintaining performance comparable to fully supervised models. These findings suggest the effectiveness of the GRN framework in optimizing segmentation performance with significantly less labeled data, offering a scalable and efficient solution for ultrasound image analysis and reducing the burdens associated with data annotation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment Zeng, Zixue Zhao, Xiaoyan Cartier, Matthew Yu, Tong Wang, Jing Meng, Xin Sheng, Zhiyu Satarpour, Maryam Cormack, John M Bean, Allison Nussbaum, Ryan Maurer, Maya Landis-Walkenhorst, Emily Kumbhare, Dinesh Kim, Kang Wasan, Ajay Pu, Jiantao Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhancement (SGE) is also developed, where the generator produces images tailored specifically for the segmentation model. Two variants of GRN were also developed, including GRN for sample-efficient learning (GRN-SEL) and GRN for semi-supervised learning (GRN-SSL). GRN's performance was evaluated using a dataset of 69 fully annotated 3D ultrasound scans from 29 subjects. The annotations included six anatomical structures: dermis, superficial fat, superficial fascial membrane (SFM), deep fat, deep fascial membrane (DFM), and muscle. Our results show that GRN-SEL with SGE reduces labeling efforts by up to 70% while achieving a 1.98% improvement in the Dice Similarity Coefficient (DSC) compared to models trained on fully labeled datasets. GRN-SEL alone reduces labeling efforts by 60%, GRN-SSL with SGE decreases labeling requirements by 70%, and GRN-SSL alone by 60%, all while maintaining performance comparable to fully supervised models. These findings suggest the effectiveness of the GRN framework in optimizing segmentation performance with significantly less labeled data, offering a scalable and efficient solution for ultrasound image analysis and reducing the burdens associated with data annotation. |
| title | Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.17690 |