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Medical Imaging Conference - Image Processing

作者： Yang, Changxing Tian, Haihong Xiang, Dehui Zhu, Weifang Shi, Fei Chen, Xinjian Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Jiangsu Peoples R China

ISBN: (纸本)9781510633940

automatic lung segmentation with severe pathology plays a significant role in the clinical application, which can save physicians' efforts to annotate lung anatomy. Since the lung has fuzzy boundary in low-dose computed tomography (CT) images, and the tracheas and other tissues generally have the similar gray value as the lung, it is a challenging task to accurately segment lung. How to extract key features and remove background features is a core problem for lung segmentation. This paper introduces a novel approach for automatic segmentation of lungs in low-dose CT images. First, we propose a contrastive attention module, which generates a pair of foreground and background attention maps to guide feature learning of lung and background separately. Second, a triplet loss is used on three feature vectors from different regions to pull the features from the full image and the lung region close whereas pushing the features from background away. Our method was validated on a clinical data set of 78 CT scans using the four-fold cross validation strategy. Experimental results showed that our method achieved more accurate segmentation results than that of state-of-the-art approaches.

关键词： automatic lung segmentation contrastive attention map triplet loss

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automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN)

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NEURAL COMPUTING & APPLICATIONS 2020年第20期32卷 15845-15855页

作者： Agnes, S. Akila Anitha, J. Peter, J. Dinesh Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Computed tomography (CT) imaging is the preferred imaging modality for diagnosing lung-related complaints. automatic lung segmentation is the most common prerequisite to develop a computerized diagnosis system for analyzing chest CT images. In this paper, a convolutional deep and wide network (CDWN) is proposed to segment lung region from the chest CT scan for further medical diagnosis. Earlier lung segmentation techniques depend on handcrafted features, and their performance relies on the features considered for segmentation. The proposed model automatically segments the lung from complete CT scan in two laps: (1) learning the required filters to extract hierarchical feature representations at convolutional layers, (2) dense prediction with spatial features through learnable deconvolutional layers. The model has been trained and evaluated with low-dose chest CT scan images on LIDC-IDRI database. The proposed CDWN reaches the average Dice coefficient of 0.95 and accuracy of 98% in segmenting the lung regions from 20 test images and maintains consistent results for all test images. The experimental results confirm that the proposed approach achieves a superior performance compared to other state-of-the-art methods for lung segmentation.

关键词： automatic lung segmentation Convolutional neural network Deep learning Image processing and analysis Medical imaging

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Hybrid automatic lung segmentation on Chest CT Scans

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IEEE ACCESS 2020年 8卷 73293-73306页

作者： Peng, Tao Xu, Thomas Canhao Wang, Yihuai Zhou, Hailing Candemir, Sema Zaki, Wan Mimi Diyana Wan Ruan, Shanq-Jang Wang, Jing Chen, Xinjian Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3220 Australia Ohio State Univ Wexner Med Ctr Columbus OH 43210 USA Natl Univ Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm INTEGRA Bangi 43600 Malaysia Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 106 Taiwan Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Soochow Univ State Key Lab Radiat Med & Protect Suzhou 215123 Peoples R China Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75390 USA

Accurate lung segmentation in chest Computed Tomography (CT) scans is a challenging problem because of variations in lung volume shape, susceptibility to partial volume effects that affect thin antero-posterior junction lines, and lack of contrast between the lung and surrounding tissues. To address the need for a robust method for lung segmentation, we present a new method, called Pixel-based two-Scan Connected Component Labeling-Convex Hull-Closed Principal Curve method (PSCCL-CH-CPC), which automatically detects lung boundaries, and surpasses state-of-the-art performance. The proposed method has two main steps: 1) an image preprocessing step to extract coarse lung contours, and 2) a refinement step to refine the coarse segmentation result on the basis of the improved principal curve model and the machine learning model. Experimental results show that the proposed method has good performance, with a Dice Similarity Coefficient (DSC) as high as 98.21%. When compared with state-of-the-art methods, our proposed method achieved superior segmentation results, with an average DSC of 96.9%.

关键词： lung Image segmentation Computed tomography Shape Machine learning Biomedical imaging Flowcharts automatic lung segmentation chest CT scans principal curve closed principal curve method machine learning

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automatic lung segmentation based on Graph Cut using a distance-constrained energy

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IET COMPUTER VISION 2018年第5期12卷 609-615页

作者： Oluyide, Oluwakorede M. Tapamo, Jules-Raymond Viriri, Serestina Univ KwaZulu Natal Sch Engn ZA-4041 Durban South Africa Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-4000 Durban South Africa

lung segmentation serves to ensure that all the parts of the lungs are considered during pulmonary image analysis by isolating the lung from the surrounding anatomy in the image. Research has shown that computed tomography (CT) images greatly improves the accuracy of the diagnosis obtained by a physician for lung cancer detection. Therefore, inspired by the success of Graph Cut in image segmentation and given that manual methods of analysing CT images are tedious and time-consuming, an automatic segmentation method based on Graph Cut is proposed which makes use of a distance-constrained energy (DCE). Graph Cut produces globally optimal solutions by modelling the image data and spatial relationship among the pixels. However, several anatomical regions in the thoracic CT image have pixel intensity values similar to the lungs, leading to results where the lung tissue and all these regions are included in the segmentation result. The global energy function is, therefore, further constrained by using the distance of pixels from a coarsely segmented region of the CT image containing the lungs. The proposed method, utilising the DCE function, shows significant improvement over using the unconstrained energy function in segmenting the lungs from the CT images using Graph Cut.

关键词： image segmentation computerised tomography medical image processing image resolution lung automatic lung segmentation Graph Cut distance-constrained energy pulmonary image analysis computed tomography images automatic segmentation method DCE pixel intensity values global energy function

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automatic lung segmentation on Thoracic CT Scans using U-Net Convolutional Network 7

Automatic Lung Segmentation on Thoracic CT Scans using U-Net...

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7th IEEE International Conference on Communication and Signal Processing (IEEE ICCSP)

作者： Shaziya, Humera Shyamala, K. Zaheer, Raniah Osmania Univ Dept CSE UCE Hyderabad India Najran Univ Dept CS Najran Saudi Arabia

ISBN: (纸本)9781538635216

lung Cancer is the most perilous cancer. Early detection of the disease can improve survival rate. Automation of detection of lung nodules aid radiologists in quickly and accurately diagnosing the disease. Developing computer aided diagnosis (CADx) systems for lung cancer is a challenging task. Several components make up CADx and one of the most significant components is lung segmentation. segmentation of lungs is an essential prerequisite to efficiently detect and classify lung nodules. lung segmentation is the process of segregating lungs region from other tissues in the CT image. Conventional methods for lung segmentation either do not accurately segments normal and abnormal lungs or rely heavily on user generated features for the lungs. Deep learning has outperformed other methods in image processing and computer vision tasks. An architecture called U-Net convolutional network has been proposed and implemented exclusively for the segmentation of biomedical images. In this study U-Net ConvNet has been implemented on lungs dataset to perform lungs segmentation. The lungs dataset consists of 267 CT images of lungs and their corresponding segmentation maps. The accuracy and loss achieved is 0.9678 and 0.0871 respectively. Hence U-Net ConvNet can be used for the segmentation of lungs in CT scans.

关键词： U-Net Convolutional Networks (ConvNet) Deep Learning lung Cancer automatic lung segmentation

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automatic lung segmentation on Thoracic CT Scans using U-Net Convolutional Network

Automatic Lung Segmentation on Thoracic CT Scans using U-Net...

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International Conference on Communication and Signal Processing

作者： Humera Shaziya K. Shyamala Raniah Zaheer Department of CSE Osmania University Department of CS Najran University

关键词： U-Net Convolutional Networks (ConvNet) Deep Learning lung Cancer automatic lung segmentation

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A-LugSeg: automatic and explainability-guided multi-site lung detection in chest X-ray images

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Peng, Tao Gu, Yidong Ye, Zhenyu Cheng, Xiuxiu Wang, Jing Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Med Artificial Intelligence & Automat Lab 2280 Inwood Rd Dallas TX 75390 USA Nanjing Med Univ Affiliated Suzhou Hosp Suzhou Municipal Hosp Dept Med Ultrasound Suzhou 215002 Jiangsu Peoples R China Soochow Univ Affiliated Hosp 2 Dept Radiol 1055 San Xiang Rd Suzhou Jiangsu Peoples R China Soochow Univ Affiliated Childrens Hosp Dept Ultrasound Suzhou 215003 Jiangsu Peoples R China

Large variations in anatomical shape and size, too much overlap between anatomical structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x-rays (CXR) a challenging problem. In this paper, we propose an automatic method called A-LugSeg that consists of two subnetworks for lung segmentation in CXRs. The first is a segmentation subnetwork based on a deep learning model (i.e., Mask-RCNN), which com-pletes a coarse segmentation for each input CXR image. The second is a refinement subnetwork designed to optimize the coarse segmentation result by combining an improved closed principal curve method and an enhanced machine learning, where the lung contour's explainability-guided mathematical model is expressed by the machine learning's parameters. The performance of the proposed method is evaluated on three public datasets, namely the ShenZhen hospital Chest X-ray dataset (SZCX), Japanese Society of Radiological Technology dataset (JSRT), and Montgomery County chest x-ray dataset (MC), which contain the 662 CXRs, 247 CXRs, and 138 CXRs, respectively. We used different datasets for training/validation (SZCX) and testing (SZCX/JSRT/MC). Furthermore, we used six evaluation metrics to evaluate the performance of our proposed method, including Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (omega), Accuracy (ACC), Precision, Sensitivity, and Specificity. The obtained results (DSC = 0.973, omega & nbsp;= 0.958, ACC = 0.972, and p-value for DSC < 0.001) for JSRT, (DSC = 0.971, omega & nbsp;= 0.955, ACC = 0.97, andp-value for DSC < 0.001) for MC, (DSC = 0.972, omega = 0.956, and ACC = 0.97) for hybrid datasets (JSRT + MC), and (Precision, Sensitivity, and Specificity are higher than 0.98) show the superior performance of the proposed dual subnetwork segmentation algorithm compared to the existing state of the art approaches.

关键词： automatic lung segmentation Chest radiographs Multi-site dataset Mask-RCNN Principal curve Improved adaptive closed polyline searching & nbsp algorithm Fractional-order backpropagation learning & nbsp algorithm Explainability-guided mathematical model

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H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method

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PHYSICS IN MEDICINE AND BIOLOGY 2022年第7期67卷 075006-075006页

作者： Peng, Tao Wang, Caishan Zhang, You Wang, Jing Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Med Artificial Intelligence & Automat Lab 2280 Inwood Rd Dallas TX 75390 USA Soochow Univ Affiliated Hosp 2 Dept Ultrasound Suzhou Jiangsu Peoples R China

Chest x-ray (CXR) is one of the most commonly used imaging techniques for the detection and diagnosis of pulmonary diseases. One critical component in many computer-aided systems, for either detection or diagnosis in digital CXR, is the accurate segmentation of the lung. Due to low-intensity contrast around lung boundary and large inter-subject variance, it has been challenging to segment lung from structural CXR images accurately. In this work, we propose an automatic Hybrid segmentation Network (H-SegNet) for lung segmentation on CXR. The proposed H-SegNet consists of two key steps: (1) an image preprocessing step based on a deep learning model to automatically extract coarse lung contours;(2) a refinement step to fine-tune the coarse segmentation results based on an improved principal curve-based method coupled with an improved machine learning method. Experimental results on several public datasets show that the proposed method achieves superior segmentation results in lung CXRs, compared with several state-of-the-art methods.

关键词： automatic lung segmentation chest radiographs mask-RCNN principal curve adaptive closed polyline searching method adaptive memory-based differential evolution method

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automatic lung segmentation: A comparison of anatomical and machine learning approaches.

Automatic lung segmentation: A comparison of anatomical and ...

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Intelligent Sensors, Sensor Networks and Information Processing Conference

作者： Misra, A Rudrapatna, M Sowmya, A Univ New S Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)0780388933

The aim of this work is to develop an automatic lung segmentation system, capable of segmenting the lung into Apical, Middle and Basal regions, along the axial plane of the body. An accurate segmentation of the lung is important for diagnosis of diffuse lung diseases, as well as to characterise and track particular diseases. In this paper we compare the two strategies we have developed. The anatomy based approach uses anatomical landmark detection to define the separation points between the regions, whilst the machine learning approach uses lung shape, size and location properties, to classify a given lung into the appropriate region.

关键词： learning (artificial intelligence) lung image segmentation diseases medical image processing automatic lung segmentation machine learning apical region middle region basal region lung disease diagnosis anatomical landmark detection lung shape lung size lung location lung Medical image processing image segmentation Machine Learning maladjustment lung Diseases

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Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium

Processing of CT images for analysis of diffuse lung disease...

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Medical Imaging 2008 Conference

作者： Karwoski, Ronald A. Bartholmai, Brian Zavaletta, Vanessa A. Holmes, David Robb, Richard A. Mayo Clin Coll Med Rochester MN 55905 USA Mayo Clin Coll Med Dept Radiol Rochester MN 55905 USA

ISBN: (纸本)9780819471000

The goal of lung Tissue Resource Consortium (LTRC) is to improve the management of diffuse lung diseases through a better understanding of the biology of Chronic Obstructive Pulmonary Disease (COPD) and fibrotic interstitial lung disease (ILD) including Idiopathic Pulmonary Fibrosis (IPF). Participants are subjected to a battery of tests including tissue biopsies, physiologic testing, clinical history reporting, and CT scanning of the chest. The LTRC is a repository from which investigators can request tissue specimens and test results as well as semi-quantitative radiology reports, pathology reports, and automated quantitative image analysis results from the CT scan data performed by the LTRC core laboratories. The LTRC Radiology Core Laboratory (RCL), in conjunction with the Biomedical Imaging Resource (BIR), has developed novel processing methods for comprehensive characterization of pulmonary processes on volumetric high-resolution CT scans to quantify how these diseases manifest in radiographic images. Specifically, the RCL has implemented a semi-automated method for segmenting the anatomical regions of the lungs and airways. In these anatomic regions, automated quantification of pathologic features of disease including emphysema volumes and tissue classification are performed using both threshold techniques and advanced texture measures to determine the extent and location of emphysema, ground glass opacities, "honeycombing" (HQ and "irregular linear" or "reticular" pulmonary infiltrates and normal lung. Wall thickness measurements of the trachea, and its branches to the 3(rd) and limited 4(th) order are also computed. The methods for processing, segmentation and quantification are described. The results are reviewed and verified by an expert radiologist following processing and stored in the public LTRC database for use by pulmonary researchers. To date, over 1200 CT scans have been processed by the RCL and the LTRC project is on target for recruitment of the

关键词： diffuse lung disease automatic lung segmentation emphysema

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