MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation
Journal:
arXiv
Published Date:
Apr 7, 2025
Abstract
Accurate segmentation of coronary Digital Subtraction Angiography images is
essential to diagnose and treat coronary artery diseases. Despite advances in
deep learning, challenges such as high intra-class variance and class imbalance
limit precise vessel delineation. Most existing approaches for coronary DSA
segmentation cannot address these issues. Also, existing segmentation network's
encoders do not directly generate semantic embeddings, which could enable the
decoder to reconstruct segmentation masks effectively from these well-defined
features. We propose a Supervised Prototypical Contrastive Loss that fuses
supervised and prototypical contrastive learning to enhance coronary DSA image
segmentation. The supervised contrastive loss enforces semantic embeddings in
the encoder, improving feature differentiation. The prototypical contrastive
loss allows the model to focus on the foreground class while alleviating the
high intra-class variance and class imbalance problems by concentrating only on
the hard-to-classify background samples. We implement the proposed SPCL loss
within an MSA-UNet3+: a Multi-Scale Attention-Enhanced UNet3+ architecture. The
architecture integrates key components: a Multi-Scale Attention Encoder and a
Multi-Scale Dilated Bottleneck designed to enhance multi-scale feature
extraction and a Contextual Attention Fusion Module built to keep fine-grained
details while improving contextual understanding. Experiments on a private
coronary DSA dataset show that MSA-UNet3+ outperforms state-of-the-art methods,
achieving the highest Dice coefficient and F1-score and significantly reducing
ASD and ACD. The developed framework provides clinicians with precise vessel
segmentation, enabling accurate identification of coronary stenosis and
supporting informed diagnostic and therapeutic decisions. The code will be
released at https://github.com/rayanmerghani/MSA-UNet3plus.
