Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

• URL: http://arxiv.org/abs/2311.10696v2
• Date: Fri, 29 Mar 2024 20:17:29 GMT
• Title: Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
• Authors: Xiaoyang Chen, Hao Zheng, Yuemeng Li, Yuncong Ma, Liang Ma, Hongming Li, Yong Fan,
• Abstract summary: We propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training. We devise strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data.
• Score: 9.068045557591612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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