Keywords: Analysis/Processing, Segmentation, Reproducibility challenge, multi-parametric renal MRI, AI/ML Image segmentation, Kidney
Motivation: Supervised deep learning provides state-of-the-art medical image segmentation when large labeled images are accessible. However, manual segmentation suffers from prolonged delineation.
Goal(s): In response to the 2024 ISMRM Challenge “Repeat it With Me: Reproducibility Team Challenge”, we aim to show the effectiveness of contrastive learning to find suitable initialization for segmentation with limited annotation.
Approach: We use a multi-contrast contrastive loss guided by representational constraints to learn discriminating features within multi-parametric renal MR images and fine-tune the pretrained model on segmentation tasks.
Results: Our findings validate that pretraining diminishes the needed annotation effort by 60% for different imaging sequences and enhances segmentation performance.
Impact: Multi-contrast contrastive learning reduces annotation effort to train deep-learning segmentation models, confirming prior findings in a new cohort, within the 2024 ISMRM Challenge “Repeat it With Me: Reproducibility Team Challenge” and indicating its potential to improve multi-parametric imaging workflows.
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