  Convolutional neural networks (CNNs) achieved the state-of-the-art
performance in medical image segmentation due to their ability to extract
highly complex feature representations. However, it is argued in recent studies
that traditional CNNs lack the intelligence to capture long-term dependencies
of different image regions. Following the success of applying Transformer
models on natural language processing tasks, the medical image segmentation
field has also witnessed growing interest in utilizing Transformers, due to
their ability to capture long-range contextual information. However, unlike
CNNs, Transformers lack the ability to learn local feature representations.
Thus, to fully utilize the advantages of both CNNs and Transformers, we propose
a hybrid encoder-decoder segmentation model (ConvTransSeg). It consists of a
multi-layer CNN as the encoder for feature learning and the corresponding
multi-level Transformer as the decoder for segmentation prediction. The encoder
and decoder are interconnected in a multi-resolution manner. We compared our
method with many other state-of-the-art hybrid CNN and Transformer segmentation
models on binary and multiple class image segmentation tasks using several
public medical image datasets, including skin lesion, polyp, cell and brain
tissue. The experimental results show that our method achieves overall the best
performance in terms of Dice coefficient and average symmetric surface distance
measures with low model complexity and memory consumption. In contrast to most
Transformer-based methods that we compared, our method does not require the use
of pre-trained models to achieve similar or better performance. The code is
freely available for research purposes on Github: (the link will be added upon
acceptance).
