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.

本文提出了一种名为H-DenseFormer的混合密集连接网络，它结合了卷积神经网络（CNN）和Transformer结构的表示能力，用于多模态医学图像的肿瘤分割，并通过设计轻量级Densely Connected Transformer（DCT）块显著降低计算复杂度。实验证明，该方法在性能和计算复杂度方面均优于现有方法。

H-DenseFormer: 一种高效的混合密集连接transformer用于多模态瘤分割