  For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of
segmenting each slice in a clinical case varies greatly. Previous research on
volumetric medical image segmentation in a slice-by-slice manner conventionally
use the identical 2D deep neural network to segment all the slices of the same
case, ignoring the data heterogeneity among image slices. In this paper, we
focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic
architecture network named Med-DANet based on adaptive model selection to
achieve effective accuracy and efficiency trade-off. For each slice of the
input 3D MRI volume, our proposed method learns a slice-specific decision by
the Decision Network to dynamically select a suitable model from the predefined
Model Bank for the subsequent 2D segmentation task. Extensive experimental
results on both BraTS 2019 and 2020 datasets show that our proposed method
achieves comparable or better results than previous state-of-the-art methods
for 3D MRI brain tumor segmentation with much less model complexity. Compared
with the state-of-the-art 3D method TransBTS, the proposed framework improves
the model efficiency by up to 3.5x without sacrificing the accuracy. Our code
will be publicly available soon.
