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M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection

Computers in Biology and Medicine 2024年 173卷 108291-108291页

作者： Zhou, Tao Wang, Hongwei Du, Yuhu Liu, Fengzhen Guo, Yujie Lu, Huiling School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information and Engineering Ningxia Medical University Yinchuan750004 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

Background: It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. Methods: To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. Results: The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. Conclusion: The M3YOLOv5 model can effectiv

关键词： Fracture

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Tang Lihui Wang Menglong Yang Jingwen Xu Xinyu Cheng Jian Zhang Yuemin Zhu Hongjiang Wei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China INSA Lyon CNRS Inserm Univ Lyon Lyon France School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity.

关键词： image registration Pathology Deformation Magnetic resonance imaging Anatomical structure Bioinformatics Tumors Biomedical imaging

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Tumor State-Space Network for High and Low Grade Glioma Classification

Tumor State-Space Network for High and Low Grade Glioma Clas...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Qijian Chen Lihui Wang Zeyu Deng Li Wang Chen Ye YueMin Zhu Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 University Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment.

关键词： Deep learning Accuracy Magnetic resonance imaging Signal processing Predictive models Tumors Radiomics

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Finite Element Analysis for the Effects of the Descending Aorta Tortuosity on Aortic Hemodynamics

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Procedia Computer Science 2022年 209卷 148-156页

作者： Jiapeng Li Xuehao Cao Lisheng Xu Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Large arteries in the human body, such as the aorta, are susceptible to atherosclerosis, and aortic hemodynamic analysis has been widely used to predict cardiovascular disease. In this study, we aim to explore whether the tortuosity of the descending aorta (DA) is associated with the development of atherosclerosis and to investigate how vessel curvature affects blood flow characteristics. To investigate the relationship between tortuosity of DA and atherosclerosis, six models and different degrees of tortuosity were considered and combined for a total of 36 computational models. The models were numerically calculated by finite element analysis based on Fluid-Structure Interaction (FSI), and hemodynamic parameters including blood flow velocity, Wall Shear Stress (WSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT), were calculated that are used to identify the locations of thrombosis and atherosclerosis. Our results showed that flow disturbances such as flow recirculation and secondary flow were more severe in the high-tortuosity vessels compared with the less-tortuosity vessels.

关键词： Computational Fluid Dynamics Fluid-Structure Interaction Descending Aorta Tortuosity

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data*

A Hybrid Feature Selection Method Based on Binary Differenti...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weidong Xie Wei Li Yushan Fang Yuhuan Chi Kun Yu Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China Medicine and Bioinformation Engineering Northeastern University Shenyang China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the “curse of dimensionality” and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm’s variation process to balance the algorithm’s search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated.

关键词： Drugs Correlation coefficient Quantization (signal) Filtering Redundancy Evolutionary computation Filtering algorithms

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Segmentation of pulmonary vessels based on MSFM method 22

Segmentation of pulmonary vessels based on MSFM method

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Wang, Xiaowei Cheng, Liying Huang, Danyang Gao, Xuanshuang Liang, Daili He, Liuye Zhang, Zhimei Li, Nan Tan, Wenjun School of Physical Science and Technology Shenyang Normal University Shenyang110034 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110189 China

ISBN: (纸本)9781728162676

Accurate segmentation of pulmonary blood vessels from CT images is of great significance for lung disease detection and segmentation of other lung structures. Manual segmentation is difficult to accurately segment vascular tissue for various reasons. Therefore, in view of the existing problems and shortcomings of the existing lung vessel segmentation method, a more efficient lung vessel segmentation algorithm is proposed, that is, the multi-template fast marching method (MSFM algorithm). Firstly, it used the hole filling and maximum inter-class variance algorithm for preprocess. In the process, the lung parenchyma is extracted from the chest CT, and then the lung blood vessels are extracted in the lung parenchymal area using the MSFM algorithm. In the extraction process, the threshold and gradient are used to limit the progress of the process and the lung blood vessels are more accurately segmented. Through experimental verification, the accuracy of lung blood vessel segmentation based on MSFM algorithm is improved. © 2021 IEEE.

关键词： Blood vessels

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Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus image Quality Enhancement

arXiv

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arXiv 2023年

作者： Hou, Qingshan Cao, Peng Wang, Jiaqi Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China DAMO Academy Alibaba Group China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low-quality fundus images and style inconsistency potentially increase uncertainty in the diagnosis of fundus disease and even lead to misdiagnosis by ophthalmologists. Most of the existing image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications. In this paper, we tackle image quality enhancement in a fully unsupervised setting, i.e., neither paired images nor high-quality images. To this end, we explore the potential of the self-supervised task for improving the quality of fundus images without the requirement of high-quality reference images. Specifically, we construct multiple patch-wise domains via an auxiliary pre-trained quality assessment network and a style clustering. To achieve robust low-quality image enhancement and address style inconsistency, we formulate two self-supervised domain adaptation tasks to disentangle the features of image content, low-quality factor and style information by exploring intrinsic supervision signals within the low-quality images. Extensive experiments are conducted on EyeQ and Messidor datasets, and results show that our DASQE method achieves new state-of-the-art performance when only low-quality images are available. © 2023, CC BY.

关键词： image enhancement

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3D PET/CT Tumor Lesion Segmentation Based on nnUNet with GCN Refinement

arXiv

引用

arXiv 2023年

作者： Xue, Hengzhi Fang, Qingqing Yao, Yudong Teng, Yueyang College of Medicine and Biological Information Engineering Northeastern University Shenyang110004 China Department of Electrical and Computer Engineering Steven Institute of Technology HobokenNJ07102 United States Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China

Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as over-segmentation and under-segmentation. Therefore, to address such issues, we propose a post-processing method based on a graph convolutional neural network (GCN) to refine inaccurate segmentation parts and improve the overall segmentation accuracy. Firstly, nnUNet is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certainty and uncertainty nodes establish the nodes of a graph neural network. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected for uncertain nodes to form edges. The highly uncertain nodes are taken as the subsequent refinement targets. Secondly, the nnUNet result of the certainty nodes is used as label to form a semi-supervised graph network problem, and the uncertainty part is optimized through training the GCN network to improve the segmentation performance. This describes our proposed nnUNet-GCN segmentation framework. We perform tumor segmentation experiments on the PET/CT dataset in the MICCIA2022 autoPET challenge. Among them, 30 cases are randomly selected for testing, and the experimental results show that the false positive rate is effectively reduced with nnUNet-GCN refinement. In quantitative analysis, there is an improvement of 2.12 % on the average Dice score, 6.34 on 95 % Hausdorff Distance (HD95), and 1.72 on average symmetric surface distance (ASSD). The quantitative and qualitative evaluation results show that GCN post-processing methods can effectively improve tumor segmentation performance. Copyright © 2023, The Authors. All rights reserved.

关键词： image segmentation

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Xiang Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781665401265

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries. © 2021 IEEE.

关键词： Tumors

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Self-supervised Noise2noise Method Utilizing Corrupted images with a Modular Network for LDCT Denoising

arXiv

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arXiv 2023年

作者： Zhu, Yuting He, Qiang Yao, Yudong Teng, Yueyang College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07102 United States

Deep learning is a very promising technique for low-dose computed tomography (LDCT) image denoising. However, traditional deep learning methods require paired noisy and clean datasets, which are often difficult to obtain. This paper proposes a new method for performing LDCT image denoising with only LDCT data, which means that normal-dose CT (NDCT) is not needed. We adopt a combination including the self-supervised noise2noise model and the noisy-as-clean strategy. First, we add a second yet similar type of noise to LDCT images multiple times. Note that we use LDCT images based on the noisy-as-clean strategy for corruption instead of NDCT images. Then, the noise2noise model is executed with only the secondary corrupted images for training. We select a modular U-Net structure from several candidates with shared parameters to perform the task, which increases the receptive field without increasing the parameter size. The experimental results obtained on the Mayo LDCT dataset show the effectiveness of the proposed method compared with that of state-of-the-art deep learning methods. The developed code is available at https://***/XYuan01/Self-supervised-Noise2Noise-for-LDCT. Copyright © 2023, The Authors. All rights reserved.

关键词： image denoising

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