BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

• URL: http://arxiv.org/abs/2401.00722v2
• Date: Mon, 30 Sep 2024 04:23:54 GMT
• Title: BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation
• Authors: Libin Lan, Pengzhou Cai, Lu Jiang, Xiaojuan Liu, Yongmei Li, Yudong Zhang,
• Abstract summary: We propose a hybrid yet effective CNN-Transformer network, named BRAU-Net++, for an accurate medical image segmentation task. Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure. Our proposed approach surpasses other state-of-the-art methods including its baseline: BRAU-Net.
• Score: 11.986549780782724
• License:
• Abstract: Accurate medical image segmentation is essential for clinical quantification, disease diagnosis, treatment planning and many other applications. Both convolution-based and transformer-based u-shaped architectures have made significant success in various medical image segmentation tasks. The former can efficiently learn local information of images while requiring much more image-specific inductive biases inherent to convolution operation. The latter can effectively capture long-range dependency at different feature scales using self-attention, whereas it typically encounters the challenges of quadratic compute and memory requirements with sequence length increasing. To address this problem, through integrating the merits of these two paradigms in a well-designed u-shaped architecture, we propose a hybrid yet effective CNN-Transformer network, named BRAU-Net++, for an accurate medical image segmentation task. Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity. Furthermore, this network restructures skip connection by incorporating channel-spatial attention which adopts convolution operations, aiming to minimize local spatial information loss and amplify global dimension-interaction of multi-scale features. Extensive experiments on three public benchmark datasets demonstrate that our proposed approach surpasses other state-of-the-art methods including its baseline: BRAU-Net under almost all evaluation metrics. We achieve the average Dice-Similarity Coefficient (DSC) of 82.47, 90.10, and 92.94 on Synapse multi-organ segmentation, ISIC-2018 Challenge, and CVC-ClinicDB, as well as the mIoU of 84.01 and 88.17 on ISIC-2018 Challenge and CVC-ClinicDB, respectively.

Related papers

• BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation [0.0]
  This paper proposes an innovative U-shaped network called BEFUnet, which enhances the fusion of body and edge information for precise medical image segmentation. The BEFUnet comprises three main modules, including a novel Local Cross-Attention Feature (LCAF) fusion module, a novel Double-Level Fusion (DLF) module, and dual-branch encoder. The LCAF module efficiently fuses edge and body features by selectively performing local cross-attention on features that are spatially close between the two modalities.
  arXiv  Detail & Related papers  (2024-02-13T21:03:36Z)
• ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation [7.955518153976858]
  We propose an advanced 2D feature extraction method by combining the convolutional neural network and Transformer architectures. Our method is shown with better segmentation accuracy, especially on small organs.
  arXiv  Detail & Related papers  (2024-01-27T05:58:36Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation [93.88170217725805]
  We propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features. Our evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-12-08T18:59:57Z)
• BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images [83.82141604007899]
  BCS-Net is a novel network for automatic COVID-19 lung infection segmentation from CT images. BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder.
  arXiv  Detail & Related papers  (2022-07-17T08:54:07Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [78.01570371790669]
  Medical image segmentation is an essential prerequisite for developing healthcare systems. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard. We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
  arXiv  Detail & Related papers  (2021-02-08T16:10:50Z)
• Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation [32.96604621259756]
  We propose an extension of U-Net for medical image segmentation. We take full advantages of U-Net, Squeeze and Excitation (SE) block, bi-directional ConvLSTM (BConvLSTM), and the mechanism of dense convolutions. The proposed model is evaluated on six datasets.
  arXiv  Detail & Related papers  (2020-03-10T12:29:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.