  Recently, deep learning methods have been widely used for tumor segmentation
of multimodal medical images with promising results. However, most existing
methods are limited by insufficient representational ability, specific modality
number and high computational complexity. In this paper, we propose a hybrid
densely connected network for tumor segmentation, named H-DenseFormer, which
combines the representational power of the Convolutional Neural Network (CNN)
and the Transformer structures. Specifically, H-DenseFormer integrates a
Transformer-based Multi-path Parallel Embedding (MPE) module that can take an
arbitrary number of modalities as input to extract the fusion features from
different modalities. Then, the multimodal fusion features are delivered to
different levels of the encoder to enhance multimodal learning representation.
Besides, we design a lightweight Densely Connected Transformer (DCT) block to
replace the standard Transformer block, thus significantly reducing
computational complexity. We conduct extensive experiments on two public
multimodal datasets, HECKTOR21 and PI-CAI22. The experimental results show that
our proposed method outperforms the existing state-of-the-art methods while
having lower computational complexity. The source code is available at
https://github.com/shijun18/H-DenseFormer.
