MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

• URL: http://arxiv.org/abs/2303.14444v2
• Date: Tue, 19 Sep 2023 14:03:10 GMT
• Title: MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
• Authors: Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner and Klaus H. Maier-Hein
• Abstract summary: Current practices limit model training and supervised pre-training to one or a few similar datasets. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions.
• Score: 1.146419670457951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available annotated data. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions to train a single model for a comprehensive structure segmentation. Our results demonstrate improved segmentation performance compared to previous related approaches, systematically, also compared to single dataset training using state-of-the-art methods, especially for lesion segmentation and other challenging structures. We show that MultiTalent also represents a powerful foundation model that offers a superior pre-training for various segmentation tasks compared to commonly used supervised or unsupervised pre-training baselines. Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance. The code and model weights will be published here: [tba]

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