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Huang H, Wang Y, Cai M, Wang R, Wen F, Hu X. Adaptive temporal compression for reduction of computational complexity in human behavior recognition. Sci Rep 2024; 14:10560. [PMID: 38720020 DOI: 10.1038/s41598-024-61286-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.
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Affiliation(s)
- Haixin Huang
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China
| | - Yuyao Wang
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China
| | - Mingqi Cai
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China
| | - Ruipeng Wang
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China
| | - Feng Wen
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, China
| | - Xiaojie Hu
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, China.
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Li L, Dai F, Huang B, Wang S, Dou W, Fu X. AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction. Sensors (Basel) 2024; 24:1261. [PMID: 38400419 PMCID: PMC10892531 DOI: 10.3390/s24041261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/05/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively.
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Affiliation(s)
- Lecheng Li
- School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China; (L.L.); (B.H.); (S.W.)
| | - Fei Dai
- School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China; (L.L.); (B.H.); (S.W.)
| | - Bi Huang
- School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China; (L.L.); (B.H.); (S.W.)
| | - Shuai Wang
- School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China; (L.L.); (B.H.); (S.W.)
| | - Wanchun Dou
- State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing 210008, China;
| | - Xiaodong Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;
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Huang H, Chen P, Wen J, Lu X, Zhang N. Multiband seizure type classification based on 3D convolution with attention mechanisms. Comput Biol Med 2023; 166:107517. [PMID: 37778214 DOI: 10.1016/j.compbiomed.2023.107517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/03/2023]
Abstract
Electroencephalogram (EEG) signal contains important information about abnormal brain activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy EEG signal classification methods mainly extract features from the perspective of a single domain, which cannot effectively utilize the spatial domain information in EEG signals. The redundant information in EEG signals will affect the learning features with the increase of convolution layer and multi-domain features, resulting in inefficient learning and a lack of distinguishing features. To tackle these issues, we propose an end-to-end 3D convolutional multiband seizure-type classification model based on attention mechanisms. Specifically, to process preprocessed electroencephalogram (EEG) data, a multilevel wavelet decomposition is applied to obtain the joint distribution information in the two-dimensional time-frequency domain across multiple frequency bands. Subsequently, this information is transformed into three-dimensional spatial data based on the electrode configuration. Discriminative joint activity features in the time, frequency, and spatial domains are then extracted by a series of parallel 3D convolutional sub-networks, where 3D channels and spatial attention mechanisms improve the ability to learn critical global and local information. A multi-layer perceptron is finally implemented to integrate the extracted features and further map them to the classification results. Experimental results on the TUSZ dataset, the world's largest publicly available seizure corpus, show that 3D-CBAMNet significantly outperforms the state-of-the-art methods, indicating effectiveness in the seizure type classification task.
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Affiliation(s)
- Hui Huang
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Peiyu Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Jianfeng Wen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Xuzhe Lu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Nan Zhang
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
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Esteve Brotons MJ, Lucendo FJ, Javier RJ, Garcia-Rodriguez J. Shot Boundary Detection with 3D Depthwise Convolutions and Visual Attention. Sensors (Basel) 2023; 23:7022. [PMID: 37631559 PMCID: PMC10457897 DOI: 10.3390/s23167022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/20/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023]
Abstract
Shot boundary detection is the process of identifying and locating the boundaries between individual shots in a video sequence. A shot is a continuous sequence of frames that are captured by a single camera, without any cuts or edits. Recent investigations have shown the effectiveness of the use of 3D convolutional networks to solve this task due to its high capacity to extract spatiotemporal features of the video and determine in which frame a transition or shot change occurs. When this task is used as part of a scene segmentation use case with the aim of improving the experience of viewing content from streaming platforms, the speed of segmentation is very important for live and near-live use cases such as start-over. The problem with models based on 3D convolutions is the large number of parameters that they entail. Standard 3D convolutions impose much higher CPU and memory requirements than do the same 2D operations. In this paper, we rely on depthwise separable convolutions to address the problem but with a scheme that significantly reduces the number of parameters. To compensate for the slight loss of performance, we analyze and propose the use of visual self-attention as a mechanism of improvement.
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Hu Z, Mao J, Yao J, Bi S. 3D network with channel excitation and knowledge distillation for action recognition. Front Neurorobot 2023; 17:1050167. [PMID: 37033413 PMCID: PMC10076829 DOI: 10.3389/fnbot.2023.1050167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
Modern action recognition techniques frequently employ two networks: the spatial stream, which accepts input from RGB frames, and the temporal stream, which accepts input from optical flow. Recent researches use 3D convolutional neural networks that employ spatiotemporal filters on both streams. Although mixing flow with RGB enhances performance, correct optical flow computation is expensive and adds delay to action recognition. In this study, we present a method for training a 3D CNN using RGB frames that replicates the motion stream and, as a result, does not require flow calculation during testing. To begin, in contrast to the SE block, we suggest a channel excitation module (CE module). Experiments have shown that the CE module can improve the feature extraction capabilities of a 3D network and that the effect is superior to the SE block. Second, for action recognition training, we adopt a linear mix of loss based on knowledge distillation and standard cross-entropy loss to effectively leverage appearance and motion information. The Intensified Motion RGB Stream is the stream trained with this combined loss (IMRS). IMRS surpasses RGB or Flow as a single stream; for example, HMDB51 achieves 73.5% accuracy, while RGB and Flow streams score 65.6% and 69.1% accuracy, respectively. Extensive experiments confirm the effectiveness of our proposed method. The comparison with other models proves that our model has good competitiveness in behavior recognition.
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Affiliation(s)
- Zhengping Hu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China
- *Correspondence: Zhengping Hu
| | - Jianzeng Mao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Jianxin Yao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Shuai Bi
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
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Chen Y, Lin Y, Xu X, Ding J, Li C, Zeng Y, Liu W, Xie W, Huang J. Classification of lungs infected COVID-19 images based on inception-ResNet. Comput Methods Programs Biomed 2022; 225:107053. [PMID: 35964421 PMCID: PMC9339166 DOI: 10.1016/j.cmpb.2022.107053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/18/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival. METHODS The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques. RESULTS The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness. CONCLUSION With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.
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Affiliation(s)
- Yunfeng Chen
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
| | - Yalan Lin
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Xiaodie Xu
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Jinzhen Ding
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Chuzhao Li
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Yiming Zeng
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
| | - Weili Liu
- Software School, Xinjiang University, Urumqi 830091, China
| | - Weifang Xie
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China; Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China
| | - Jianlong Huang
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China; Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China
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Di Benedetto M, Carrara F, Tafuri B, Nigro S, De Blasi R, Falchi F, Gennaro C, Gigli G, Logroscino G, Amato G. Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources. Comput Biol Med 2022; 148:105937. [PMID: 35985188 DOI: 10.1016/j.compbiomed.2022.105937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 06/28/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative syndrome whose clinical diagnosis remains a challenging task especially in the early stage of the disease. Currently, the presence of frontal and anterior temporal lobe atrophies on magnetic resonance imaging (MRI) is part of the diagnostic criteria for bvFTD. However, MRI data processing is usually dependent on the acquisition device and mostly require human-assisted crafting of feature extraction. Following the impressive improvements of deep architectures, in this study we report on bvFTD identification using various classes of artificial neural networks, and present the results we achieved on classification accuracy and obliviousness on acquisition devices using extensive hyperparameter search. In particular, we will demonstrate the stability and generalization of different deep networks based on the attention mechanism, where data intra-mixing confers models the ability to identify the disorder even on MRI data in inter-device settings, i.e., on data produced by different acquisition devices and without model fine tuning, as shown from the very encouraging performance evaluations that dramatically reach and overcome the 90% value on the AuROC and balanced accuracy metrics.
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Affiliation(s)
- Marco Di Benedetto
- Institute of Information Science and Technologies "Alessandro Faedo" (ISTI), National Research Council (CNR), Pisa (PI), Italy.
| | - Fabio Carrara
- Institute of Information Science and Technologies "Alessandro Faedo" (ISTI), National Research Council (CNR), Pisa (PI), Italy
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari "Aldo Moro", Tricase (LE), Italy; Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari'Aldo Moro', Bari (BA), Italy
| | - Salvatore Nigro
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari "Aldo Moro", Tricase (LE), Italy; Institute of Nanotechnology (NANOTEC), National Research Council (CNR), Lecce (LE), Italy
| | - Roberto De Blasi
- Department of Radiology, "Pia Fondazione Cardinale G. Panico", Tricase, Lecce (LE), Italy
| | - Fabrizio Falchi
- Institute of Information Science and Technologies "Alessandro Faedo" (ISTI), National Research Council (CNR), Pisa (PI), Italy
| | - Claudio Gennaro
- Institute of Information Science and Technologies "Alessandro Faedo" (ISTI), National Research Council (CNR), Pisa (PI), Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council (CNR), Lecce (LE), Italy; Department of Mathematics and Physics "Ennio De Giorgi", University of Salento, Campus Ecotekne, Lecce (LE), Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari "Aldo Moro", Tricase (LE), Italy; Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari'Aldo Moro', Bari (BA), Italy
| | - Giuseppe Amato
- Institute of Information Science and Technologies "Alessandro Faedo" (ISTI), National Research Council (CNR), Pisa (PI), Italy
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Hai J, Qiao K, Chen J, Liang N, Zhang L, Yan B. Multi-view features integrated 2D\3D Net for glomerulopathy histologic types classification using ultrasound images. Comput Methods Programs Biomed 2021; 212:106439. [PMID: 34695734 DOI: 10.1016/j.cmpb.2021.106439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/18/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Early diagnoses and rational therapeutics of glomerulopathy can control progression and improve prognosis. The gold standard for the diagnosis of glomerulopathy is pathology by renal biopsy, which is invasive and has many contraindications. We aim to use renal ultrasonography for histologic classification of glomerulopathy. METHODS Ultrasonography can present multi-view sections of kidney, thus we proposed a multi-view and cross-domain integration strategy (CD-ConcatNet) to obtain more effective features and improve diagnosis accuracy. We creatively apply 2D group convolution and 3D convolution to process multiple 2D ultrasound images and extract multi-view features of renal ultrasound images. Cross-domain concatenation in each spatial resolution of feature maps is applied for more informative feature learning. RESULTS A total of 76 adult patients were collected and divided into training dataset (56 cases with 515 images) and validation dataset (20 cases with 180 images). We obtained the best mean accuracy of 0.83 and AUC of 0.8667 in the validation dataset. CONCLUSION Comparison experiments demonstrate that our designed CD-ConcatNet achieves the best classification performance and has great superiority on histologic types diagnosis. Results also prove that the integration of multi-view ultrasound images is beneficial for histologic classification and ultrasound images can indeed provide discriminating information for histologic diagnosis.
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Affiliation(s)
- Jinjin Hai
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, China
| | - Kai Qiao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, China
| | - Jian Chen
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, China
| | - Ningning Liang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, China
| | - Lijie Zhang
- Department of Nephrology in First Affiliated Hospital of Zhengzhou University, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, China.
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Meng L, Tian Y, Bu S. Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys 2019; 21:144-157. [PMID: 31793212 PMCID: PMC6964770 DOI: 10.1002/acm2.12784] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 10/08/2019] [Accepted: 11/04/2019] [Indexed: 12/23/2022] Open
Abstract
Purpose Liver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The contrast between the liver tumors and healthy tissues in CT images is low and the boundary is blurred; (b) The image of liver tumor is complex and diversified in size, shape, and location. Methods To solve the above problems, this paper focused on the human liver and liver tumor segmentation algorithm based on convolutional neural network (CNN), and specially designed a three‐dimensional dual path multiscale convolutional neural network (TDP‐CNN). To balance the performance of segmentation and requirement of computational resources, the dual path was used in the network, then the feature maps from both paths were fused at the end of the paths. To refine the segmentation results, we used conditional random fields (CRF) to eliminate the false segmentation points in the segmentation results to improve the accuracy. Results In the experiment, we used the public dataset liver tumor segmentation (LiTS) to analyze the segmentation results qualitatively and quantitatively. Ground truth segmentation of liver and liver tumor was manually labeled by an experienced radiologist. Quantitative metrics were Dice, Hausdorff distance, and average distance. For the segmentation results of liver tumor, Dice was 0.689, Hausdorff distance was 7.69, and the average distance was 1.07; for the segmentation results of the liver, Dice was 0.965, Hausdorff distance was 29.162, and the average distance was 0.197. Compared with other liver and liver tumor segmentation algorithms in Medical Image Computing and Intervention (MICCAI) 2017 competition, our method of liver segmentation ranked first, and liver tumor segmentation ranked second. Conclusions The experimental results showed that the proposed algorithm had good performance in both liver and liver tumor segmentation.
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Affiliation(s)
- Lu Meng
- College of Information Science and Engineering, Northeastern University, ShenYang, China
| | - Yaoyu Tian
- College of Information Science and Engineering, Northeastern University, ShenYang, China
| | - Sihang Bu
- College of Information Science and Engineering, Northeastern University, ShenYang, China
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