  Hierarchical transformers have achieved significant success in medical image
segmentation due to their large receptive field and capabilities of effectively
leveraging global long-range contextual information. Convolutional neural
networks (CNNs) can also deliver a large receptive field by using large
kernels, enabling them to achieve competitive performance with fewer model
parameters. However, CNNs incorporated with large convolutional kernels remain
constrained in adaptively capturing multi-scale features from organs with large
variations in shape and size due to the employment of fixed-sized kernels.
Additionally, they are unable to utilize global contextual information
efficiently. To address these limitations, we propose Dynamic Large Kernel
(DLK) and Dynamic Feature Fusion (DFF) modules. The DLK module employs multiple
large kernels with varying kernel sizes and dilation rates to capture
multi-scale features. Subsequently, a dynamic selection mechanism is utilized
to adaptively highlight the most important spatial features based on global
information. Additionally, the DFF module is proposed to adaptively fuse
multi-scale local feature maps based on their global information. We integrate
DLK and DFF in a hierarchical transformer architecture to develop a novel
architecture, termed D-Net. D-Net is able to effectively utilize a multi-scale
large receptive field and adaptively harness global contextual information.
Extensive experimental results demonstrate that D-Net outperforms other
state-of-the-art models in the two volumetric segmentation tasks, including
abdominal multi-organ segmentation and multi-modality brain tumor segmentation.
Our code is available at https://github.com/sotiraslab/DLK.
