MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

by   Constantin Ulrich, et al.

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]


Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training

The difficulties in both data acquisition and annotation substantially r...

Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging

Most state-of-the-art techniques for medical image segmentation rely on ...

TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors

Medical image segmentation is routinely performed to isolate regions of ...

Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

Universal lesion detection from computed tomography (CT) slices is impor...

Adversarial normalization for multi domain image segmentation

Image normalization is a critical step in medical imaging. This step is ...

Please sign up or login with your details

Forgot password? Click here to reset