Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation
<|reference_start|>Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation: U-Net has been widely used for segmenting abdominal organs, achieving promising performance. However, when it is used for multi-organ segmentation, first, it may be limited in exploiting global long-range contextual information due to the implementation of standard convolutions. Second, the use of spatial-wise downsampling (e.g., max pooling or strided convolutions) in the encoding path may lead to the loss of deformable or discriminative details. Third, features upsampled from the higher level are concatenated with those that persevered via skip connections. However, repeated downsampling and upsampling operations lead to misalignments between them and their concatenation degrades segmentation performance. To address these limitations, we propose Dynamically Calibrated Convolution (DCC), Dynamically Calibrated Downsampling (DCD), and Dynamically Calibrated Upsampling (DCU) modules, respectively. The DCC module can utilize global inter-dependencies between spatial and channel features to calibrate these features adaptively. The DCD module enables networks to adaptively preserve deformable or discriminative features during downsampling. The DCU module can dynamically align and calibrate upsampled features to eliminate misalignments before concatenations. We integrated the proposed modules into a standard U-Net, resulting in a new architecture, termed Dynamic U-Net. This architectural design enables U-Net to dynamically adjust features for different organs. We evaluated Dynamic U-Net in two abdominal multi-organ segmentation benchmarks. Dynamic U-Net achieved statistically improved segmentation accuracy compared with standard U-Net. Our code is available at https://github.com/sotiraslab/DynamicUNet.<|reference_end|>
@article{yang2024dynamic,
    title={Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ
  Segmentation},
    author={Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras},
    journal={Medical Imaging 2025: Computer-Aided Diagnosis},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07303},
    primaryClass={eess.IV cs.CV}
}