Large-Kernel Attention for 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2207.11225v1
• Date: Tue, 19 Jul 2022 16:32:55 GMT
• Title: Large-Kernel Attention for 3D Medical Image Segmentation
• Authors: Hao Li, Yang Nan, Javier Del Ser, Guang Yang
• Abstract summary: In this paper, a novel large- kernel (LK) attention module is proposed to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of convolution and self-attention are combined in the proposed LK attention module, including local contextual information, long-range dependence, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into FCNs such as U-Net.
• Score: 14.76728117630242
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of convolution and self-attention are combined in the proposed LK attention module, including local contextual information, long-range dependence, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into FCNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance. The performance improvement due to the proposed LK attention module was also statistically validated.

Related papers

• MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network [0.0]
  This study proposes the MBDRes-U-Net model using the three-dimensional (3D) U-Net framework, which integrates multibranch residual blocks and fused attention into the model. The computational burden of the model is reduced by the branch strategy, which effectively uses the rich local features in multimodal images.
  arXiv  Detail & Related papers  (2024-11-04T09:03:43Z)
• 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)
• 3D Medical Image Segmentation based on multi-scale MPU-Net [5.393743755706745]
  This paper proposes a tumor segmentation model MPU-Net for patient volume CT images. It is inspired by Transformer with a global attention mechanism. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results.
  arXiv  Detail & Related papers  (2023-07-11T20:46:19Z)
• 3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation [6.127298607534532]
  Deep learning has recently emerged to improve brain tumor segmentation. Transformers have been leveraged to address the limitations of convolutional networks. We propose a 3D Transformer-based segmentation approach.
  arXiv  Detail & Related papers  (2023-04-28T02:11:29Z)
• Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism [27.08977505280394]
  A coordination-spatial attention generation adversarial network (CASP-GAN) based on the cycle-consistent generative adversarial network (CycleGAN) is proposed. The performance of the generator is optimized by introducing the Coordinate Attention (CA) module and the Spatial Attention (SA) module. The ability to extract the structure information and the detailed information of the original medical image can help generate the desired image with higher quality.
  arXiv  Detail & Related papers  (2023-04-17T08:34:41Z)
• Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution [56.622832383316215]
  We propose a novel multi-head convolutional attention module to super-resolve CT and MRI scans. Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple input tensors. We introduce multiple attention heads, each head having a distinct receptive field size corresponding to a particular reduction rate for the spatial attention.
  arXiv  Detail & Related papers  (2022-04-08T07:56:55Z)
• Category Guided Attention Network for Brain Tumor Segmentation in MRI [6.685945448824158]
  We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net) In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity.
  arXiv  Detail & Related papers  (2022-03-29T09:22:29Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation [53.187152856583396]
  We propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features.
  arXiv  Detail & Related papers  (2020-10-09T08:22:54Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.