An improved residual U-Net with morphological-based loss function for automatic liver segmentation in computed tomography

Lv, Peiqing; Wang, Jinke; Zeng, Xiangyang; Ji, Chunlei; Zhou, Lubiao; Wang, Haiying

doi:10.3934/mbe.2022066

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Automatic segmentation via cascade and conditional random fields was proposed by Chen et al [20]. Lv et al [21] created an improved version of the ResU‐Net model for automatic liver segmentation from CT scans. Sabir et al [22] put forward an automatic approach based on the ResU‐Net architecture applied to CT scans.…”

Section: Related Workmentioning

confidence: 99%

Transformer Skip‐Fusion Based SwinUNet for Liver Segmentation From CT Images

Kumar,

Vinod Kumar

2024

Int J Imaging Syst Tech

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Liver segmentation is a crucial step in medical image analysis and is essential for diagnosing and treating liver diseases. However, manual segmentation is time‐consuming and subject to variability among observers. To address these challenges, a novel liver segmentation approach, SwinUNet with transformer skip‐fusion is proposed. This method harnesses the Swin Transformer's capacity to model long‐range dependencies efficiently, the U‐Net's ability to preserve fine spatial details, and the transformer skip‐fusion's effectiveness in enabling the decoder to learn intricate features from encoder feature maps. In experiments using the 3DIRCADb and CHAOS datasets, this technique outperformed traditional CNN‐based methods, achieving a mean DICE coefficient of 0.988% and a mean Jaccard coefficient of 0.973% by aggregating the results obtained from each dataset, signifying outstanding agreement with ground truth. This remarkable accuracy in liver segmentation holds significant promise for improving liver disease diagnosis and enhancing healthcare outcomes for patients with liver conditions.

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Section: Related Workmentioning

confidence: 99%

Transformer Skip‐Fusion Based SwinUNet for Liver Segmentation From CT Images

Kumar,

Vinod Kumar

2024

Int J Imaging Syst Tech

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“…Lv proposes an improved liver CT segmentation method based on iRes-Unet. In view of the shortcomings of Unet in performance, BN layer is introduced to eliminate the covariate shift within the network, improve the generalization and convergence speed, and the Residual-module is introduced to enhance the edge thinning 29 . Li first uses Gaussian filter to de-noise the image, then processes the CT image through ecological operation, and finally uses Res Unet model to improve the segmentation loss and improve the segmentation accuracy and speed 30 .…”

Section: Related Workmentioning

confidence: 99%

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

Zang,

Liang,

et al. 2023

Sci Rep

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As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.

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“…The flexibility and modular design of the U-Net architecture allows for its seamless integration with other network architectures or modules, offering a pathway for further enhancement and customization. A previous study used the residual module to replace the original convolution module of U-Net in order to speed up the convergence of the model, which demonstrated faster model convergence efficiency in the segmentation of liver CT images ( 17 ). 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 ).…”

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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An improved residual U-Net with morphological-based loss function for automatic liver segmentation in computed tomography

Cited by 9 publications

References 31 publications

Transformer Skip‐Fusion Based SwinUNet for Liver Segmentation From CT Images

Transformer Skip‐Fusion Based SwinUNet for Liver Segmentation From CT Images

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

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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