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Abstract #4283

Semi-supervised 3D Myocardial Segmentation for Whole-Heart Joint T1/T2 mapping with Self-trained nnUNet

Carlota Rivera1,2, Alina Hua3, René M Botnar1,2,3,4,5, and Claudia Prieto1,2,3,4
1IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile, 2Millennium Institute iHEALTH, Santiago, Chile, 3School of Biomedical Engineering, King’s College London, London, United Kingdom, 4School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 5Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Synopsis

Keywords: Diagnosis/Prediction, Analysis/Processing, Segmentation, 3D Mapping, Joint T1/T2

Motivation: Segmenting 3D whole-heart T1/T2 mapping data is time-intensive and automatic solutions require significant manual effort for labeling. A semi-supervised approach using unlabeled data could reduce this workload, accelerate segmentation, and enhance efficiency.

Goal(s): To automate 3D myocardial segmentation for whole-heart joint T1/T2 mapping using nnUNet in a semi-supervised manner, using limited labeled data.

Approach: A 3D nnUNet with pseudo-labeling is implemented for myocardial segmentation in joint T1/T2 mapping, combining manual labels and generated pseudo-labels to optimize the process.

Results: This semi-supervised approach achieved a Dice above 0.877 using only 10 manual labels, with 80% fewer labels than supervised methods.

Impact: Semi-supervised 3D nnUNet enables accurate myocardial segmentation in 3D whole-heart joint T1/T2 mapping, even with limited labeled data. This could improve efficiency, reduce manual segmentation effort, and accelerate the diagnosis of myocardial diseases.

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Keywords