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arXiv · 2303.14444

MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

Constantin Ulrich·Fabian Isensee·Tassilo Wald·Maximilian Zenk·Michael Baumgartner·Klaus H. Maier-Hein
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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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Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner, Klaus H. Maier-Hein. 2023-09-19. MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation. https://doi.org/10.1007/978-3-031-43898-1_62

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