Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network

Czipczer, Vanda; Manno-Kovacs, Andrea

doi:10.1016/j.neucom.2022.07.024

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…Brain SAB-Net, EMED-Unet, GAU-Net, U-Net++, MILD-Net, SD-Unet, KiU-Net, 3D U-Net [16], [17], [18], [19], [20], [21], [22], [23] Lung U-Net11, EMED-Unet, SMR-Unet, GA-Unet, Sharp U-Net, Recurrent Residual 3D U-Net [18], [24], [25], [26], [27] Liver U-Net, SAB-Net, GA-Unet, 3 D RP-Unet, ELU-Net [28], [17], [25], [29], [30],…”

Section: Category U-net Architectures Papersmentioning

confidence: 99%

An overview of image processing in biomedicine using U-Net convolutional neural network architecture

Komosar,

Stefanović,

Sladojević

2024

J Comp Foren Sci

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Image processing in biomedicine is a very broad field, which includes both medical and technical significance. The aim of this paper is to investigate the current trends in the domain of application of U-Net architecture in the period from 2018 to 2023. The PRISMA framework was used for the systematic literature review, and 4 research questions were asked. For the most part, U-Net architectures are used that can process complex high-resolution images in the fastest way in the context of semantic segmentation. Previous work in image processing has focused on overcoming problems such as the complexity of different architectures, image loss, image resolution, and quality, as well as the size of datasets and noise reduction. The most frequently used groups of datasets are BraTS, Data Science Bowl, and ISIC Challenge. The best general Dice score was obtained for the LUNA16, VESSEL12, and Kaggle Lung datasets with 0.98. It is concluded that the application of the U-Net network is growing, with a focus on solving specific challenges in the context of a certain modality and segment of biomedicine.

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Section: Category U-net Architectures Papersmentioning

confidence: 99%

An overview of image processing in biomedicine using U-Net convolutional neural network architecture

Komosar,

Stefanović,

Sladojević

2024

J Comp Foren Sci

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“…Although it has high computational efficiency and consumes less resources, it is difficult to deal with complex scenes in images [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional image segmentation methods mainly include: region-based segmentation, edge-based segmentation, threshold-based segmentation and segmentation based on specific theoretical tools, etc. Although it has high computational efficiency and consumes less resources, it is difficult to deal with complex scenes in images [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning

Hou,

Zhao,

Cui

et al. 2024

PLoS ONE

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The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets

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“…Image segmentation is the act of splitting an image into many separate sections, each with its own set of attributes, so that these features show consistency or similarity within the same region while displaying obvious disparities between regions. Among the often utilized techniques are threshold-based image segmentation methods [2][3][4], region-based image segmentation methods [5][6][7], edge-based image segmentation methods [8][9][10], and graph theory-based segmentation methods [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Image segmentation algorithm based on cumulative residual Tsallis entropy

Tian

Liu

Fan

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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An algorithm which applies Cumulative Residual Tsallis Entropy (CRTE) to threshold image segmentation is proposed. Cumulative residual Tsallis entropy is an uncertainty measure generalized from Shannon entropy. The approach utilizes the cumulative residual Tsallis entropy function as the objective function, specifies the threshold segmentation criterion, and finds the optimal cumulative residual Tsallis entropy function solution. This method is applied to Berkeley Segmentation Dataset and compared with several classical segmentation methods on the same dataset. Experiment findings suggest that this algorithm is capable of producing improved segmentation results.

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Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network

Cited by 14 publications

References 39 publications

An overview of image processing in biomedicine using U-Net convolutional neural network architecture

An overview of image processing in biomedicine using U-Net convolutional neural network architecture

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning

Image segmentation algorithm based on cumulative residual Tsallis entropy

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