Lightweight image segmentation for echocardiography

• URL: http://arxiv.org/abs/2509.03631v1
• Date: Wed, 03 Sep 2025 18:33:28 GMT
• Title: Lightweight image segmentation for echocardiography
• Authors: Anders Kjelsrud, Lasse Løvstakken, Erik Smistad, Håvard Dalen, Gilles Van De Vyver,
• Abstract summary: We developed a lightweight U-Net that achieves statistically equivalent performance to nnU-Net on CAMUS.<n>Our analysis revealed that simple affine augmentations and deep supervision drive performance, while complex augmentations and large model capacity offer diminishing returns.
• Score: 0.45360533198417524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of the left ventricle in echocardiography can enable fully automatic extraction of clinical measurements such as volumes and ejection fraction. While models configured by nnU-Net perform well, they are large and slow, thus limiting real-time use. We identified the most effective components of nnU-Net for cardiac segmentation through an ablation study, incrementally evaluating data augmentation schemes, architectural modifications, loss functions, and post-processing techniques. Our analysis revealed that simple affine augmentations and deep supervision drive performance, while complex augmentations and large model capacity offer diminishing returns. Based on these insights, we developed a lightweight U-Net (2M vs 33M parameters) that achieves statistically equivalent performance to nnU-Net on CAMUS (N=500) with Dice scores of 0.93/0.85/0.89 vs 0.93/0.86/0.89 for LV/MYO/LA ($p>0.05$), while being 16 times smaller and 4 times faster (1.35ms vs 5.40ms per frame) than the default nnU-Net configuration. Cross-dataset evaluation on an internal dataset (N=311) confirms comparable generalization.

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