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Organ segmentation from computed tomography images using the 3d convolutional neural network: a systematic review

INTERNATIONAL JOURNAL OF MULTIMEdIA INFORMATION RETRIEVAL 2022年第3期11卷 315-331页

作者： Ilesanmi, Ademola E. Ilesanmi, Taiwo Idowu, Oluwagbenga P. Torigian, drew A. Udupa, Jayaram K. Univ Penn Dept Radiol Med Image Proc Grp Goddard Bldg3710 Hamilton Walk6th Floor Philadelphia PA 19104 USA Natl Populat Commiss Abuja Nigeria Sichuan Univ West China Hosp Chengdu Peoples R China

Computed tomography images are scans that combine a series of X-rays with computer processing techniques to display organs in the body. Recently, 3d CNN models have become effective in tasks relating to recognition, delineation, and classification. Therefore we propose a review to summarize different 3d CNN algorithms for segmenting organs in computed tomography images. This work systematically applies a two-stage procedure for review. A thorough screening of abstracts and titles to ascertain their relevance was done. Research papers published in the academic repositories were selected, analyzed, and reviewed. Insight relating to 3d organ segmentation is provided, with content such as database usage, disadvantages, and advantages. A comparison of two accuracies was carried out with a graph depicting database categories. Important insights, limitations, observations, and future directions were elucidated. After careful investigation, we observe that the encoder-decoder network is predominant for segmentation. The encoder-decoder framework provides a seamless procedure to segment CT images. A prediction of future trends with insightful recommendations for researchers is proposed. Finally, findings suggest that CNN algorithms produce good accuracies despite their limitations.

关键词： 3d convolutional neural network Computed tomography images Segmentation of organs Image preprocessing

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Isotropic Brain MRI Reconstruction from Orthogonal Scans Using 3d convolutional neural network

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SENSORS 2024年第20期24卷 6639页

作者： Tian, Jinsha Xiao, Canjun Zhu, Hongjin Chengdu Technol Univ Sch Big Data & Artificial Intelligence Chengdu 611730 Peoples R China Chengdu Technol Univ Digital Twin Lab Chengdu 611730 Peoples R China

As an alternative to true isotropic 3d imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3d volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged processing times, and the necessity for manual feature extraction. Motivated by the exceptional representational ability and automatic feature extraction of convolutional neural networks (CNNs), in this work, we present an end-to-end isotropic MRI reconstruction strategy based on deep learning. The proposed method is based on 3d convolutional neural networks (3d CNNs), which can effectively capture the 3d structural features of MRI volumes and accurately predict potential structure. In addition, the proposed method takes multiple orthogonal scans as input and thus enables the model to use more complementary information from different dimensions for precise inference. Experimental results show that the proposed algorithm achieves promising performance in terms of both quantitative and qualitative assessments. In addition, it can process a 3d volume with a size of 256 x 256 x 256 in less than 1 min with the support of an NVIdIA GeForce GTX 1080Ti GPU, which suggests that it is not only a quantitatively superior method but also a practical one.

关键词： 3d convolutional neural network isotropic reconstruction magnetic resonance imaging super-resolution orthogonal scans

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Smoke Video detection Algorithm Based On 3d convolutional neural network 34

Smoke Video Detection Algorithm Based On 3D Convolutional Ne...

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34th Chinese Control and decision Conference (CCdC)

作者： Shi, Zhen Sun, Rui Huo, Mingge Harbin Engn Univ Inst Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Beijing Electromech Engn Res Inst Beijing Beijing Peoples R China

ISBN: (纸本)9781665478960

Smoke detection can play a key role in the early stage of a fire. Traditional video smoke detection mainly extracts image features of smoke and uses machine learning methods to classify and recognize smoke. However, smoke video is an image of continuous frames, and adjacent frames of images are related to each other. If time complexity is ignored and only spatial features are considered, the key features cannot be fully extracted. By combining the 3d convolutional neural network and introducing the attention mechanism, the temporal and spatial features of the smoke can be effectively weighted and merged, and the 3x1x1 and 1x3x3 convolution kernels are used instead of the 3x3x3 convolution kernels. Reduce model parameters. In order to eliminate most of the non-smoke areas to reduce the time complexity of the system, the MobileNetV2-SSd algorithm is used to do a priori score to locate the suspected smoke areas. Experimental results show that compared to traditional smoke detection methods and 2d smoke detection convolutional networks, the accuracy of smoke detection has been improved.

关键词： Smoke video detection 3d convolutional neural network Temporal and spatial characteristics Attention mechanism Mobilenetv2-SSd

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Link Prediction Based on 3d convolutional neural network

Link Prediction Based on 3D Convolutional Neural Network

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Shu, Jian Li, JiaHao Zhang, XuePei Nanchang Hangkong Univ Sch Software Nanchang Jiangxi Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

The dynamic evolution of networks opportunities is very complex, which brings challenges to link prediction. By analyzing the mobile characteristics of nodes, a new link prediction algorithm of opportunistic networks based on a 3d convolutional neural network (3d-CNN) was proposed. It uses the evolution information of the whole network topology to predict the possible changes of local links. In this paper, network attributes under different dimensions, such as time, space, correlation between nodes, are combined to represent opportunistic networks. The pattern classification method is adopted to predict future link combinations between multinodes. Using a 3d convolutional neural network to extract spatiotemporal features from multi-dimensional attributes can affect local link status. According to the extracted features, the evolution trend of future links can be inferred, and the multinode link prediction of opportunistic networks can be realized. We have designed experiments under different conditions to find the best parameters. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

关键词： opportunistic network 3d convolutional neural network multi-node pair link prediction multidimensional attributed integrated representation

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A new 3d convolutional neural network (3d-CNN) framework for multimedia event detection

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SIGNAL IMAGE ANd VIdEO PROCESSING 2021年第4期15卷 779-787页

作者： Kanagaraj, Kaavya Priya, G. G. Lakshmi VIT SITE Vellore Tamil Nadu India

Multimedia event detection has received a great deal of interest due to developments in video technology and an increase in multimedia data. However, complexities of video content such as noisy, overlapping, repeated interaction between individuals, and various scenes are becoming difficult for characterizing the subjects and concepts. In particular, Internet users find it difficult to search for a specified event. To solve the above problem, a method is proposed that best suits for event detection, demonstrating the 3d convolutional neural network (3d-CNN) structure to accomplish promising performance in multimedia event classification. To take an advantage of motion content of the event in the video, temporal axis is considered. Both the feature extraction and classification are incorporated in this model. Experiments are carried out on the Columbia Consumer Video benchmark dataset, and results are compared with other existing works.

关键词： Multimedia event detection 3d convolutional neural network Feature extraction Classification Mean average precision Columbia consumer video

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Optimising a 3d convolutional neural network for head and neck computed tomography segmentation with limited training data

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PHYSICS & IMAGING IN RAdIATION ONCOLOGY 2022年 22卷 44-50页

作者： Henderson, Edward G. A. Osorio, Eliana M. Vasquez van Herk, Marcel Green, Andrew F. Univ Manchester Oxford Rd Manchester M13 9PL England Christie NHS Fdn Trust Radiotherapy Related Res Manchester M20 4BX England

Background and purpose: convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce. The aim of this study was to develop a CNN capable of accurate head and neck (HN) 3d auto-segmentation of planning CT scans using a small training dataset (34 CTs). Materials and Method: Elements of our custom CNN architecture were varied to optimise segmentation performance. We tested and evaluated the impact of: using multiple contrast channels for the CT scan input at specific soft tissue and bony anatomy windows, resize vs. transpose convolutions, and loss functions based on overlap metrics and cross-entropy in different combinations. Model segmentation performance was compared with the inter-observer deviation of two doctors' gold standard segmentations using the 95th percentile Hausdorff distance and mean distance-to-agreement (mdTA). The best performing configuration was further validated on a popular public dataset to compare with state-of-the-art (SOTA) auto-segmentation methods. Results: Our best performing CNN configuration was competitive with current SOTA methods when evaluated on the public dataset with mdTA of (0.81 +/- 0.31) mm for the brainstem, (0.20 +/- 0.08) mm for the mandible, (0.77 +/- 0.14) mm for the left parotid and (0.81 +/- 0.28) mm for the right parotid. Conclusions: Through careful tuning and customisation we trained a 3d CNN with a small dataset to produce segmentations of HN OARs with an accuracy that is comparable with inter-clinician deviations. Our proposed model performed competitively with current SOTA methods.

关键词： Limited data 3d convolutional neural network CT scan auto-segmentation

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An end-to-end 3d convolutional neural network for decoding attentive mental state

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neural networkS 2021年 144卷 129-137页

作者： Zhang, Yangsong Cai, Huan Nie, Li Xu, Peng Zhao, Sirui Guan, Cuntai Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China Univ Elect Sci & Technol China Sch Life Sci & Technol MOE Key Lab Neuroinformat Chengdu Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore Singapore Minist Educ Key Lab Cognit & Personal Chongqing Peoples R China

The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention deficit and Hyperactivity disorder (AdHd). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (dL) methods in the research of brain-computer interface (BCI) field, a 3d representation of EEG signal was introduced into attention detection task, and a 3d convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3d convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3d CNN model for detecting attentive mental state. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： EEG Attention BCI deep learning 3d convolutional neural network

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Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3d convolutional neural network

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COMPUTERS IN BIOLOGY ANd MEdICINE 2023年第1期160卷 107022-107022页

作者： Shen, Mingkan Wen, Peng Song, Bo Li, Yan Univ Southern Queensland Sch Engn Toowoomba Australia Univ Southern Queensland Sch Math Phys & Comp Toowoomba Australia

Schizophrenia (ScZ) is a devastating mental disorder of the human brain that causes a serious impact of emotional inclinations, quality of personal and social life and healthcare systems. In recent years, deep learning methods with connectivity analysis only very recently focused into fMRI data. To explore this kind of research into electroencephalogram (EEG) signal, this paper investigates the identification of ScZ EEG signals using dy-namic functional connectivity analysis and deep learning methods. A time-frequency domain functional con-nectivity analysis through cross mutual information algorithm is proposed to extract the features in alpha band (8-12 Hz) of each subject. A 3d convolutional neural network technique was applied to classify the ScZ subjects and health control (HC) subjects. The LMSU public ScZ EEG dataset is employed to evaluate the proposed method, and a 97.74 +/- 1.15% accuracy, 96.91 +/- 2.76% sensitivity and 98.53 +/- 1.97% specificity results were achieved in this study. In addition, we also found not only the default mode network region but also the con-nectivity between temporal lobe and posterior temporal lobe in both right and left side have significant differ-ence between the ScZ and HC subjects.

关键词： ScZ EEG Cross mutual information 3d convolutional neural network default mode network

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Reviewing 3d convolutional neural network approaches for medical image segmentation

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HELIYON 2024年第6期10卷 e27398页

作者： Ilesanmi, Ademola E. Ilesanmi, Taiwo O. Ajayi, Babatunde O. Univ Penn 3710 Hamilton Walk6th Floor Philadelphia PA 19104 USA Natl Populat Commiss Abuja Nigeria Natl Astron Res Inst Thailand Chiang Mai 50180 Thailand

Background: convolutional neural networks (CNNs) assume pivotal roles in aiding clinicians in diagnosis and treatment decisions. The rapid evolution of imaging technology has established three-dimensional (3d) CNNs as a formidable framework for delineating organs and anomalies in medical images. The prominence of 3d CNN frameworks is steadily growing within medical image segmentation and classification. Thus, our proposition entails a comprehensive review, encapsulating diverse 3d CNN algorithms for the segmentation of medical image anomalies and organs. Methods: This study systematically presents an exhaustive review of recent 3d CNN methodologies. Rigorous screening of abstracts and titles were carried out to establish their relevance. Research papers disseminated across academic repositories were meticulously chosen, analyzed, and appraised against specific criteria. Insights into the realm of anomalies and organ segmentation were derived, encompassing details such as network architecture and achieved accuracies. Results: This paper offers an all -encompassing analysis, unveiling the prevailing trends in 3d CNN segmentation. In-depth elucidations encompass essential insights, constraints, observations, and avenues for future exploration. A discerning examination indicates the preponderance of the encoder -decoder network in segmentation tasks. The encoder -decoder framework affords a coherent methodology for the segmentation of medical images. Conclusion: The findings of this study are poised to find application in clinical diagnosis and therapeutic interventions. despite inherent limitations, CNN algorithms showcase commendable accuracy levels, solidifying their potential in medical image segmentation and classification endeavors.

关键词： 3d convolutional neural network Medical images Segmentation of abnormalities and organs

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MBANet: A 3d convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images

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BIOMEdICAL SIGNAL PROCESSING ANd CONTROL 2023年第Part1期80卷 000-000页

作者： Cao, Yuan Zhou, Weifeng Zang, Min An, dianlong Feng, Yan Yu, Bin Qingdao Univ Sci & Technol Coll Informat Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230027 Peoples R China

More than half of brain tumors are malignant tumors, so there is a need for fast and accurate segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images. Traditional 2d brain tumor segmentation methods seriously ignore the spatial context features of brain tumor MRI images, so how to achieve accurate segmentation of brain tumor regions with multiple modalities is the main problem. The paper proposes a 3d convolutional neural network with 3d multi-branch attention -MBANet. First, the optimized shuffle unit is used to form the basic unit (BU) module of MBANet. In the BU module, the group convolution is used to perform convolution operation after the input channel is split, and the channel shuffle is used to scramble the con-volutional channels after fusion. Then, MBANet uses a novel multi-branch 3d Shuffle Attention (SA) module as the attention layer in the encoder. The 3d SA module groups along the channel dimension and divides the feature maps into small features. For each small feature, the 3d SA module builds both channel attention and spatial attention while adopting the BU module. In addition, in order to recover the resolution of the upsampling se-mantic features better, a 3d SA module is also used in the skip connection of MBANet. Experiments on the BraTS 2018 and BraTS 2019 show that the dice of ET, WT and TC reach 80.18%, 89.80%, 85.47% and 78.21%, 89.79%, 83.04%, respectively. The excellent segmentation performance shows that MBANet is significantly improved compared with other state-of-the-art methods.

关键词： MBANet Brain tumor segmentation 3d multi -branch attention 3d convolutional neural network

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