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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.10696 |
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| _version_ | 1866914735225569280 |
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| author | Chen, Xiaoyang Zheng, Hao Li, Yuemeng Ma, Yuncong Ma, Liang Li, Hongming Fan, Yong |
| author_facet | Chen, Xiaoyang Zheng, Hao Li, Yuemeng Ma, Yuncong Ma, Liang Li, Hongming Fan, Yong |
| contents | A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building such a model typically demands a large, diverse, and fully annotated dataset, which is challenging to obtain due to the labor-intensive nature of data curation. To address this challenge, we propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training, substantially reducing the cost of developing a versatile model. We devise strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data, including label ambiguity and modality, dataset, and class imbalances. Experimental results on a multi-modal dataset compiled from eight different sources for abdominal structure segmentation have demonstrated the effectiveness and superior performance of our method compared to state-of-the-art alternative approaches. We anticipate that its cost-saving features, which optimize the utilization of existing annotated data and reduce annotation efforts for new data, will have a significant impact in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10696 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation Chen, Xiaoyang Zheng, Hao Li, Yuemeng Ma, Yuncong Ma, Liang Li, Hongming Fan, Yong Computer Vision and Pattern Recognition A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building such a model typically demands a large, diverse, and fully annotated dataset, which is challenging to obtain due to the labor-intensive nature of data curation. To address this challenge, we propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training, substantially reducing the cost of developing a versatile model. We devise strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data, including label ambiguity and modality, dataset, and class imbalances. Experimental results on a multi-modal dataset compiled from eight different sources for abdominal structure segmentation have demonstrated the effectiveness and superior performance of our method compared to state-of-the-art alternative approaches. We anticipate that its cost-saving features, which optimize the utilization of existing annotated data and reduce annotation efforts for new data, will have a significant impact in the field. |
| title | Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.10696 |