UCATR: Based on CNN and Transformer Encoding and Cross-Attention Decoding for Lesion Segmentation of Acute Ischemic Stroke in Non-contrast Computed Tomography Images

Luo, Chun; Zhang, Jing; Chen, Xinglin; Tang, Yinhao; Weng, Xiechuan; Xu, Fan

doi:10.1109/embc46164.2021.9630336

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…The images are learnt for a global representation. The hierarchical Swin Transformer with an offset window as an encoder to represent the image spatial context features and design a decoder with a patch extended layer for upsampling operations to recover the spatial resolution of the feature graph [21] . SWin-Unet's architecture is shown in Figure 2.…”

Section: Swin-unet: Unet-like Pure Transformer For Tooth Segmentationmentioning

confidence: 99%

“…Traditional CNN shows good performance in image segmentation, but due to the limitation of convolutional operations, the global and remote semantic information interaction cannot be learned well. SWin-Unet builds a symmetric encoder-decoder structure with jump connections, implements a local to global self-attention mechanism, and develops a patch expanding layer without convolution and interpolation operations (patch expanding layer) to increases upsampling and feature dimension, which outperforms convolution based methods [20,21] .…”

Section: Introductionmentioning

confidence: 99%

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Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

et al. 2022

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Panoramic radiographs can assist dentist to quickly evaluate patients' overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.

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Section: Swin-unet: Unet-like Pure Transformer For Tooth Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

et al. 2022

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“…Based on the U-Net structure, Cao et al progressed the SWIN-Unit transformer algorithm, superior to U-Net and other models in multiorgan and heart segmentation tasks, with a DSC value of 0.9 [ 52 ]. In ischemic stroke lesion analysis, the model, including CNN and transformer for encoding and the multihead cross-attention (MHCA) module for decoding, leads to stroke lesion morphology and edges with a Dice of 73.58% [ 53 ]. Transformers can be introduced to process data with different scanner models or multimodal data.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Cui

Fan

Yang

et al. 2022

BioMed Research International

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Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

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“…The skip connection in the developed U-shape model is composed of a residual atrous pyramid pooling module, a position-sensitive axial attention module, and a hierarchical context-attention module. Luo and colleagues [148] designed a model named UCATR to segment acute ischemic stroke lesions using the NCCT CT dataset. The proposed model is composed of a CNN-transformer encoder and a cross-attention decoder.…”

Section: Segmentationmentioning

confidence: 99%

Recent Progress in Transformer-based Medical Image Analysis

Liu¹,

Shen²

2022

Preprint

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The transformer has dominated the natural language processing (NLP) field for a long time. Recently, the transformer-based method has been adopted into the computer vision (CV) field and shows promising results. As an important branch of the CV field, medical image analysis joins the wave of the transformer-based method rightfully. In this review, we illustrate the principle of the attention mechanism, and the detailed structures of the transformer, and depict how the transformer is adopted into medical image analysis. We organize the transformer-based medical image analysis applications in a sequence of different tasks, including classification, segmentation, synthesis, registration, localization, detection, captioning, and denoising. For the mainstream classification and segmentation tasks, we further divided the corresponding works based on different medical imaging modalities. The datasets corresponding to the related works are also organized. We include thirteen modalities and more than twenty objects in our work.

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UCATR: Based on CNN and Transformer Encoding and Cross-Attention Decoding for Lesion Segmentation of Acute Ischemic Stroke in Non-contrast Computed Tomography Images

Cited by 14 publications

References 11 publications

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Recent Progress in Transformer-based Medical Image Analysis

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