LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images
Journal:
arXiv
Published Date:
Jan 27, 2025
Abstract
UNet and its variants have widespread applications in medical image
segmentation. However, the substantial number of parameters and computational
complexity of these models make them less suitable for use in clinical settings
with limited computational resources. To address this limitation, we propose a
novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and
the Tokenized Shift Block in a lightweight manner, combining them with a
dynamic weight multi-loss design for efficient dynamic weight allocation. The
Light Conv Block effectively captures features with a low parameter count by
combining standard convolutions with depthwise separable convolutions. The
Tokenized Shift Block optimizes feature representation by shifting and
capturing deep features through a combination of the Spatial Shift Block and
depthwise separable convolutions. Dynamic adjustment of the loss weights at
each layer approaches the optimal solution and enhances training stability. We
validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results
demonstrate that LSU-Net outperforms most state-of-the-art segmentation
architectures.
