UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network

Fang, Kun; He, Baochun; Liu, Libo; Hu, Haoyu; Fang, Chihua; Huang, Xuguang; Jia, Fucang

doi:10.21037/qims-22-544

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Similarly, MRFormer optimises capturing the target and its surrounding context by embedding multi-head attention mechanisms and residual depth-wise convolutional networks within the skip connections. Fang et al demonstrate that MR-Former outperforms the basic U-Net model in pancreatic CT image segmentation [47]. Additionally, MDAG-Net incorporates a novel multi-dimensional attention module (MDAG) to bolster skip connections, capturing context information and precisely defining target features more effectively [48].…”

Section: Skip Connection Variationsmentioning

confidence: 99%

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

Zhang,

Achuthan,

Himel

2024

IEEE Access

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In medical image analysis, segmenting pancreatic CT images presents a significant challenge due to the complex anatomy of the pancreas and the generally low contrast of these images. Accurate pancreas segmentation is crucial in clinical scenarios, particularly for the diagnosis and treatment of pancreatic cancer. The U-Net architecture and its variations have achieved significant progress in deep learningbased image segmentation, especially in the context of pancreatic CT image segmentation. However, there is a noticeable gap in the comprehensive evaluation of their performance, limitations, and potential improvements specifically in this area. This systematic review aims to address this gap in the literature, focusing particularly on U-Net and its variants in pancreatic CT image segmentation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes relevant studies published since 2019 in the field of pancreatic segmentation. The findings illuminate the current limitations of these methods and establish a theoretical foundation for future research directions.

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Section: Skip Connection Variationsmentioning

confidence: 99%

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

Zhang,

Achuthan,

Himel

2024

IEEE Access

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“…In response to the above problems, various methods based on deep learning [7][8][9][10] have become the mainstream in the field of medical image segmentation due to their powerful feature learning, endto-end training, adaptability and generalization. At present, many researchers focus on fully convolutional networks (FCN) [11] and U-shaped structures [12][13][14][15], which usually employ encoder-decoder frameworks and thus perform well in simulating local features of images. However, due to the limited receptive field of convolution operations, these methods are often difficult to capture the global dependence of features, which is particularly important in semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

Zheng,

Jiang,

et al. 2024

IEEE Access

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Accurate segmentation of organs and lesions from medical images holds paramount importance in aiding physicians with diagnosis and monitor diseases. At present, the widespread application of deeplearning in medical image segmentation is primarily attributed to its exceptional feature extraction capability. Nonetheless, due to blurred target boundary, wide range of changes and chaotic background, the segmentation of medical images is still faced with great challenges. To address these issues, we present a multi-level feature integration network (MFI-Net) with SE-Res2Conv encoder for jaw cyst segmentation. Specifically, we replace the original convolution operation with SE-Res2Conv to better maintain model's capacity for extracting features across multiple scales. Then, a novel context extractor module including multi-scale pooling block (MPB) and position attention module (PAM), which aims to generate more discriminative features. Finally, a multi-level feature integration block (MFIB) is implemented within the decoder to efficiently integrate low-level detail features with high-level semantic features. Numerous experiments were conducted on both the original and augmented datasets of jaw cyst to demonstrate the advantages of MFI-Net, with results consistently superior to all competitors. The Dice, IoU and Jaccard values of our method reached 93.06%, 93.47%, 87.06% in the original database and 91.25%, 91.94%, 84.06% in the augmented database. Furthermore, the computational efficiency of MFI-Net is impressive, with a speed of 106 FPS at the input size of 3×256×256 on a NVIDIA RTX6000 graphics card.

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“…In recent years, in order to retain the features of small targets in the deep network, previous studies have also adopted the squeezing and excitation module for various image-processing tasks, which has improved the segmentation effect compared with that of the state-of-the art model ( 18 , 19 ). In addition, researchers have further extended the capabilities of the original U-Net by combining U-Net with Transformer in order to fully utilize the low-level features, which in turn can enhance the global features and reduce the semantic gap between the encoding and decoding stages ( 20 ). However, these methods suffer from the problems of excessive parameter amount, with the large number of samples required for training the model hindering convergence, which limits the scope of potential applications in radiotherapy image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture

Wang,

Hu,

Chen

et al. 2024

Quant Imaging Med Surg

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Background Brain metastases present significant challenges in radiotherapy due to the need for precise tumor delineation. Traditional methods often lack the efficiency and accuracy required for optimal treatment planning. This paper proposes an improved U-Net model that uses a position attention module (PAM) for automated segmentation of gross tumor volumes (GTVs) in computed tomography (CT) simulation images of patients with brain metastases to improve the efficiency and accuracy of radiotherapy planning and segmentation. Methods We retrospectively collected CT simulation imaging datasets of patients with brain metastases from two centers, which were designated as the training and external validation datasets. The U-Net architecture was enhanced by incorporating a PAM into the transition layer, which improved the automated segmentation capability of the U-Net model. With cross-entropy loss employed as the loss function, the samples from the training dataset underwent training. The model’s segmentation performance on the external validation dataset was assessed using metrics including the Dice similarity coefficient (DSC), intersection over union (IoU), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and Hausdorff distance (HD). Results The proposed automated segmentation model demonstrated promising performance on the external validation dataset, achieving a DSC of 0.753±0.172. In terms of evaluation metrics (including the DSC, IoU, accuracy, sensitivity, MCC, and HD), the model outperformed the standard U-Net, which had a DSC of 0.691±0.142. The proposed model produced segmentation results that were closer to the ground truth and could reveal more detailed features of brain metastases. Conclusions The PAM-improved U-Net model offers considerable advantages in the automated segmentation of the GTV in CT simulation images for patients with brain metastases. Its superior performance in comparison with the standard U-Net model supports its potential for streamlining and improving the accuracy of radiotherapy. With its ability to produce segmentation results consistent with the ground truth, the proposed model holds promise for clinical adoption and provides a reference for radiation oncologists to make more informed GTV segmentation decisions.

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UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network

Cited by 7 publications

References 20 publications

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture

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