SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation

• URL: http://arxiv.org/abs/2408.00496v1
• Date: Thu, 1 Aug 2024 12:05:02 GMT
• Title: SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation
• Authors: Shengbo Tan, Zeyu Zhang, Ying Cai, Daji Ergu, Lin Wu, Binbin Hu, Pengzhang Yu, Yang Zhao,
• Abstract summary: State-of-the-art methods, particularly those utilizing transformers, have been prominently adopted in 3D semantic segmentation. However, plain vision transformers encounter challenges due to their neglect of local features and their high computational complexity. We propose SegStitch, an innovative architecture that integrates transformers with denoising ODE blocks.
• Score: 15.811141677039224
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical imaging segmentation plays a significant role in the automatic recognition and analysis of lesions. State-of-the-art methods, particularly those utilizing transformers, have been prominently adopted in 3D semantic segmentation due to their superior performance in scalability and generalizability. However, plain vision transformers encounter challenges due to their neglect of local features and their high computational complexity. To address these challenges, we introduce three key contributions: Firstly, we proposed SegStitch, an innovative architecture that integrates transformers with denoising ODE blocks. Instead of taking whole 3D volumes as inputs, we adapt axial patches and customize patch-wise queries to ensure semantic consistency. Additionally, we conducted extensive experiments on the BTCV and ACDC datasets, achieving improvements up to 11.48% and 6.71% respectively in mDSC, compared to state-of-the-art methods. Lastly, our proposed method demonstrates outstanding efficiency, reducing the number of parameters by 36.7% and the number of FLOPS by 10.7% compared to UNETR. This advancement holds promising potential for adapting our method to real-world clinical practice. The code will be available at https://github.com/goblin327/SegStitch

Related papers

• Segmenting Medical Images with Limited Data [7.83073107607804]
  We present a semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS) The DEMS features an encoder-decoder architecture and incorporates the developed online automatic augmenter (OAA) and residual robustness enhancement (RRE) blocks. Under extreme data shortage scenarios, our DEMS achieves 16.85% and 10.37% improvement in dice score compared with the U-Net and top-performed state-of-the-art method, respectively.
  arXiv  Detail & Related papers  (2024-07-12T11:41:18Z)
• Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning [52.249748801637196]
  3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning. Recent advances in deep learning have significantly enhanced fully supervised medical image segmentation. We propose a novel probabilistic-aware weakly supervised learning pipeline, specifically designed for 3D medical imaging.
  arXiv  Detail & Related papers  (2024-03-05T00:46:53Z)
• CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention [10.335899694123711]
  We introduce Context Infused Swin-UNet (CIS-UNet), a deep learning model for aortic segmentation. CIS-UNet adopts a hierarchical encoder-decoder structure comprising a CNN encoder, symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) as the bottleneck block. We trained our model on computed tomography (CT) scans from 44 patients and tested it on 15 patients. CIS-UNet outperformed the state-of-the-art SwinUNetR segmentation model, by achieving a superior mean Dice coefficient of 0.713 compared
  arXiv  Detail & Related papers  (2024-01-23T19:17:20Z)
• E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation [36.367368163120794]
  We propose a 3D medical image segmentation model, named Efficient to Efficient Network (E2ENet) It incorporates two parametrically and computationally efficient designs. It consistently achieves a superior trade-off between accuracy and efficiency across various resource constraints.
  arXiv  Detail & Related papers  (2023-12-07T22:13:37Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• 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)
• 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)
• Dynamic Linear Transformer for 3D Biomedical Image Segmentation [2.440109381823186]
  Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks. Main challenge for 3D transformer-based segmentation methods is the quadratic complexity introduced by the self-attention mechanism. We propose a novel transformer architecture for 3D medical image segmentation using an encoder-decoder style architecture with linear complexity.
  arXiv  Detail & Related papers  (2022-06-01T21:15:01Z)
• nnFormer: Interleaved Transformer for Volumetric Segmentation [50.10441845967601]
  We introduce nnFormer, a powerful segmentation model with an interleaved architecture based on empirical combination of self-attention and convolution. nnFormer achieves tremendous improvements over previous transformer-based methods on two commonly used datasets Synapse and ACDC.
  arXiv  Detail & Related papers  (2021-09-07T17:08:24Z)
• 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)

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.