Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) for 3D Medical Image Segmentation and Visualization

• URL: http://arxiv.org/abs/2406.17080v1
• Date: Mon, 24 Jun 2024 19:09:20 GMT
• Title: Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) for 3D Medical Image Segmentation and Visualization
• Authors: Siyavash Shabani, Muhammad Sohaib, Sahar A. Mohammed, Bahram Parvin,
• Abstract summary: This study introduces the Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) It integrates the output of Swin Transformers and their corresponding convolutional blocks using 3D fusion blocks. The proposed architecture has demonstrated a score of 89.73 and 7.31 for Dice and HD95, respectively.
• Score: 1.3749490831384268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision Transformers have shown superior performance to the traditional convolutional-based frameworks in many vision applications, including but not limited to the segmentation of 3D medical images. To further advance this area, this study introduces the Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net), which integrates the output of Swin Transformers and their corresponding convolutional blocks using 3D fusion blocks. The Multi-Aperture incorporates each image patch at its original resolutions with its pyramid representation to better preserve minute details. The proposed architecture has demonstrated a score of 89.73 and 7.31 for Dice and HD95, respectively, on the Synapse multi-organs dataset an improvement over the published results. The improved performance also comes with the added benefits of the reduced complexity of approximately 40 million parameters. Our code is available at https://github.com/Siyavashshabani/MFTC-Net

Related papers

• SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation [0.13654846342364302]
  We present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features. We benchmark SegFormer3D against the current SOTA models on three widely used datasets.
  arXiv  Detail & Related papers  (2024-04-15T22:12:05Z)
• 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation [5.635173603669784]
  We propose a lightweight volumetric ConvNet, termed 3D UX-Net, which adapts the hierarchical transformer using ConvNet modules for robust volumetric segmentation. Specifically, we revisit volumetric depth-wise convolutions with large kernel size (e.g. starting from $7times7times7$) to enable the larger global receptive fields, inspired by Swin Transformer.
  arXiv  Detail & Related papers  (2022-09-29T19:54:13Z)
• MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet [55.16833099336073]
  We propose to self-distill a Transformer-based UNet for medical image segmentation. It simultaneously learns global semantic information and local spatial-detailed features. Our MISSU achieves the best performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-02T07:38:53Z)
• Multimodal Token Fusion for Vision Transformers [54.81107795090239]
  We propose a multimodal token fusion method (TokenFusion) for transformer-based vision tasks. To effectively fuse multiple modalities, TokenFusion dynamically detects uninformative tokens and substitutes these tokens with projected and aggregated inter-modal features. The design of TokenFusion allows the transformer to learn correlations among multimodal features, while the single-modal transformer architecture remains largely intact.
  arXiv  Detail & Related papers  (2022-04-19T07:47:50Z)
• AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation [19.53151547706724]
  transformer-based models have drawn attention to exploring these techniques in medical image segmentation. We propose Axial Fusion Transformer UNet (AFTer-UNet), which takes both advantages of convolutional layers' capability of extracting detailed features and transformers' strength on long sequence modeling. It has fewer parameters and takes less GPU memory to train than the previous transformer-based models.
  arXiv  Detail & Related papers  (2021-10-20T06:47:28Z)
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [63.46694853953092]
  Swin-Unet is an Unet-like pure Transformer for medical image segmentation. tokenized image patches are fed into the Transformer-based U-shaped decoder-Decoder architecture.
  arXiv  Detail & Related papers  (2021-05-12T09:30:26Z)
• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation [95.51455777713092]
  Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. We propose a novel framework that efficiently bridges a bf Convolutional neural network and a bf Transformer bf (CoTr) for accurate 3D medical image segmentation.
  arXiv  Detail & Related papers  (2021-03-04T13:34:22Z)
• Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [73.98974074534497]
  We study the feasibility of using Transformer-based network architectures for medical image segmentation tasks. We propose a Gated Axial-Attention model which extends the existing architectures by introducing an additional control mechanism in the self-attention module. To train the model effectively on medical images, we propose a Local-Global training strategy (LoGo) which further improves the performance.
  arXiv  Detail & Related papers  (2021-02-21T18:35:14Z)
• Visual Transformers: Token-based Image Representation and Processing for Computer Vision [67.55770209540306]
  Visual Transformer ( VT) operates in a semantic token space, judiciously attending to different image parts based on context. Using an advanced training recipe, our VTs significantly outperform their convolutional counterparts. For semantic segmentation on LIP and COCO-stuff, VT-based feature pyramid networks (FPN) achieve 0.35 points higher mIoU while reducing the FPN module's FLOPs by 6.5x.
  arXiv  Detail & Related papers  (2020-06-05T20:49:49Z)

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.