Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks

Chen, Xiaowen; Wei, Xiaoqin; Tang, Mingyue; Liu, Aimin; Lai, Ce; Yuan-zhong, Zhu; He, Wenjing

doi:10.21037/atm-21-5822

Cited by 9 publications

(4 citation statements)

References 47 publications

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“…Weston et al (2020) [ 25 ] established a liver segmentation method based on the CNN algorithm, and the results showed that the Dice value of this method for segmenting liver CT images was 0.79. Chen et al (2021) [ 26 ] optimized the model based on the FCN algorithm and applied it to liver segmentation. The results showed that the Dice value of this method for segmenting liver CT images was 0.742.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm

Wang

Bao

Cao

et al. 2022

Contrast Media & Molecular Imaging

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This study was aimed at exploring the application of image segmentation based on full convolutional neural network (FCN) in liver computed tomography (CT) image segmentation and analyzing the clinical features of acute liver injury caused by sepsis. The Sigmoid function, encoder-decoder, and weighted cross entropy loss function were introduced and optimized based on FCN. The Dice value, precision, recall rate, volume overlap error (VOE), relative volume difference (RVD), and root mean square error (RMSE) values of the optimized algorithms were compared and analyzed. 92 patients with sepsis were selected as the research objects, and they were divided into a nonacute liver injury group (50 cases) and acute liver injury group (42 cases) based on whether they had acute liver injury. The differences in the proportion of patients with different disease histories, the proportion of patients with different infection sites, the number of organ failure, and the time of admission to intensive care unit (ICU) were compared between the two groups. It was found that the optimized window CT image Dice value after preprocessing (0.704 ± 0.06) was significantly higher than the other two methods ( P < 0.05 ). The Dice value, precision, and recall rate of the optimized-FCN algorithm were (0.826 ± 0.06), (0.91 ± 0.08), and (0.88 ± 0.09), respectively, which were significantly higher than other algorithms ( P < 0.05 ). The VOE, RVD, and RMSE values were (21.19 ± 1.97), (10.45 ± 1.02), and (0.25 ± 0.02), respectively, which were significantly lower than other algorithms ( P < 0.05 ). The proportion of patients with a history of drinking in the nonacute liver injury group was lower than that in the acute liver injury group ( P < 0.05 ), and the proportion of patients with a history of hypotension was greatly higher than that in the nonacute liver injury group ( P < 0.01 ). CT images of sepsis patients with acute liver injury showed that large areas of liver parenchyma mixed with high-density hematoma, the number of organ failures, and the length of stay in ICU were significantly higher than those in the nonacute liver injury group ( P < 0.05 ). It showed that the optimization algorithm based on FCN greatly improved the performance of CT image segmentation. Long-term drinking, low blood pressure, number of organ failures, and length of stay in ICU were all related to sepsis and acute liver injury. Conclusion in this study could provide a reference basis for the diagnosis and prognosis of acute liver injury caused by sepsis.

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Section: Discussionmentioning

confidence: 99%

Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm

Wang

Bao

Cao

et al. 2022

Contrast Media & Molecular Imaging

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“…SFF-Net (Spatial Feature Fusion Convolutional Network) was proposed by Liu et al [41]. Chen et al [42] combine enhanced Mask R-CNN and graph-cut for liver segmentation.…”

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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“…Chen, Xiaowen et al suggested a Mask R-CNN technique that outperformed the conventional Mask R-CNN techniques when evaluated using the Dice Similarity Coefficient and Medicinal Image Computing and Computer-Assisted Intervention (MICCAI) metrics [15]. Chen, Lei et al have implemented ASD, MSD, VOE, and RVD all improved using an adversarial training strategy from 27.8 to 21, 147 to 124, 0.52 to 0.46, and 0.69 to 0.73, proving the effectiveness of the suggested approach.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

CHALLAGUNDLA,

Chintalapati,

Tunuguntla

et al. 2023

Preprint

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Automatic detection and segmentation of liver tumors from CT scans is a challenging task due to their complex morphology in CT images, but advances in deep learning have made it possible to achieve high accuracy in the segmentation and detection of liver tumors, eliminating the need for manual intervention and allowing radiologists to focus more on critical diseases by giving awareness about the liver tumor at an early stage, which may save a person's life by allowing them to receive treatment sooner, possibly preventing further severity or death. In our study, we worked on different approaches to detect liver tumors from CT scans and analyzed their performance in terms of accuracy. A few of the approaches employed are using Convolutional neural networks including existing ones like U-Net, ResNet50, while we proposed a combination of multiple CNN models to obtain better results. It was observed that the combination of CNN models produced higher accuracies rather than the existing models.

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Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks

Cited by 9 publications

References 47 publications

Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm

Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm

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

Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

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