3D Medical image segmentation using parallel transformers. (June 2023) - Yan, Qingsen; Liu, Shengqiang; Xu, Songhua; Dong, Caixia; Li, Zongfang; Shi, Javen Qinfeng; Zhang, Yanning; Dai, Duwei
Highlights: We propose a novel deep neural network based on Transformer, named TransHRNet, which connects the different resolution streams in parallel and repeatedly exchanges the information across resolutions. An Effective Transformer (EffTrans) is introduced to promote the performance, which uses the group linear transformations with an expand-reduce strategy and the spatial-reduction attention layer to further reduce the resource cost. Our proposed method achieves higher performance than SoTA efficient medical image segmentation method with comparable computation cost. Abstract: Most recent 3D medical image segmentation methods adopt convolutional neural networks (CNNs) that rely on deep feature representation and achieve adequate performance. However, due to the convolutional architectures having limited receptive fields, they cannot explicitly model the long-range dependencies in the medical image. Recently, Transformer can benefit from global dependencies using self-attention mechanisms and learn highly expressive representations. Some works were designed based on the Transformers, but the existing Transformers suffer from extreme computational and memories, and they cannot take full advantage of the powerful feature representations in 3D medical image segmentation. In this paper, we aim to connect the different resolution streams in parallel and propose a novel network, named Trans former based H igh R esolution Net work (TransHRNet), with an Effective Transformer (EffTrans) block, which has sufficient feature representation even at high feature resolutions . Given a 3D image, the encoder first utilizes CNN to extract the feature representations to capture the local information, and then the different feature maps are reshaped elaborately for tokens that are fed into each Transformer stream in parallel to learn the global information and repeatedly exchange the information across streams. Unfortunately, the proposed framework based on the standard Transformer needs a huge amount of computation, thus we introduce a deep and effective Transformer to deliver better performance with fewer parameters. The proposed TransHRNet is evaluated on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV) dataset that consists of 11 major human organs and the Medical Segmentation Decathlon (MSD) dataset for brain tumor and spleen segmentation tasks. Experimental results show that it performs better than the convolutional and other related Transformer-based methods on the 3D multi-organ segmentation tasks. Code is available at https://github.com/duweidai/TransHRNet .