Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ
  Segmentation
Jin Yang Daniel S. Marcus Aristeidis Sotiras
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.