CNN-based techniques have achieved impressive outcomes in medical image segmentation but struggle to capture long-term dependencies between pixels. The Transformer, with its strong feature extraction and representation learning abilities, performs exceptionally well within the domain of medical image partitioning. However, there are still shortcomings in bridging local to global connections, resulting in occasional loss of positional information. To address this, we introduce a decoder based on dynamic convolution, called Adaptive Cascade Decoder (ACD). It can adaptively adjust the receptive field size based on medical images, adapting a set of parameters for each medical image individually. The ACD consists of an Adaptive Attention (ADA) and a Multi-Scale Convolution module (MSC). By enhancing feature extraction from local to global scales, it addresses issues of diminished contrast and fuzzy boundaries common in medical image segmentation. While increasing contextual connections, it also reduces certain parameters, thereby lowering memory consumption. Our model, T-ACD, uses the encoder backbone of TransUNet, which chunks feature maps from convolutional neural networks and feeds them as one-dimensional sequences into the Transformer. This leverages the Transformer's prowess in handling sequences, further refining the extracted features. In experiments involving heart and multi-organ segmentation, T-ACD excels in segmenting challenging areas. On the ACDC dataset, we achieve a DICE coefficient of 92.02 %. For the most challenging right ventricle, it is improved to 90.68 %, which is increased by 5.17 %. In the realm of medical segmentation, the core design of ACD can be generalized to other challenging organ segmentations.
Keywords: Adaptive; Challenging areas; Decoder; Medical image segmentation; Transformer.
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