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Khorshidi A. Tumor segmentation via enhanced area growth algorithm for lung CT images. BMC Med Imaging 2023; 23:189. [PMID: 37986046 PMCID: PMC10662793 DOI: 10.1186/s12880-023-01126-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 10/16/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Since lung tumors are in dynamic conditions, the study of tumor growth and its changes is of great importance in primary diagnosis. METHODS Enhanced area growth (EAG) algorithm is introduced to segment the lung tumor in 2D and 3D modes on 60 patients CT images from four different databases by MATLAB software. The contrast augmentation, color intensity and maximum primary tumor radius determination, thresholding, start and neighbor points' designation in an array, and then modifying the points in the braid on average are the early steps of the proposed algorithm. To determine the new tumor boundaries, the maximum distance from the color-intensity center point of the primary tumor to the modified points is appointed via considering a larger target region and new threshold. The tumor center is divided into different subsections and then all previous stages are repeated from new designated points to define diverse boundaries for the tumor. An interpolation between these boundaries creates a new tumor boundary. The intersections with the tumor boundaries are firmed for edge correction phase, after drawing diverse lines from the tumor center at relevant angles. Each of the new regions is annexed to the core region to achieve a segmented tumor surface by meeting certain conditions. RESULTS The multipoint-growth-starting-point grouping fashioned a desired consequence in the precise delineation of the tumor. The proposed algorithm enhanced tumor identification by more than 16% with a reasonable accuracy acceptance rate. At the same time, it largely assurances the independence of the last outcome from the starting point. By significance difference of p < 0.05, the dice coefficients were 0.80 ± 0.02 and 0.92 ± 0.03, respectively, for primary and enhanced algorithms. Lung area determination alongside automatic thresholding and also starting from several points along with edge improvement may reduce human errors in radiologists' interpretation of tumor areas and selection of the algorithm's starting point. CONCLUSIONS The proposed algorithm enhanced tumor detection by more than 18% with a sufficient acceptance ratio of accuracy. Since the enhanced algorithm is independent of matrix size and image thickness, it is very likely that it can be easily applied to other contiguous tumor images. TRIAL REGISTRATION PAZHOUHAN, PAZHOUHAN98000032. Registered 4 January 2021, http://pazhouhan.gerums.ac.ir/webreclist/view.action?webreclist_code=19300.
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Affiliation(s)
- Abdollah Khorshidi
- School of Paramedical, Gerash University of Medical Sciences, P.O. Box: 7441758666, Gerash, Iran.
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肖 汉, 李 焕, 冉 智, 张 启, 张 勃, 韦 羽, 祝 秀. [Corona virus disease 2019 lesion segmentation network based on an adaptive joint loss function]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:743-752. [PMID: 37666765 PMCID: PMC10477394 DOI: 10.7507/1001-5515.202206051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 05/30/2023] [Indexed: 09/06/2023]
Abstract
Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.
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Affiliation(s)
- 汉光 肖
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
| | - 焕琪 李
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
| | - 智强 冉
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
| | - 启航 张
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
| | - 勃龙 张
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
| | - 羽佳 韦
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
| | - 秀红 祝
- 重庆理工大学 两江人工智能学院(重庆 401135)School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P. R. China
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Tonneau M, Phan K, Manem VSK, Low-Kam C, Dutil F, Kazandjian S, Vanderweyen D, Panasci J, Malo J, Coulombe F, Gagné A, Elkrief A, Belkaïd W, Di Jorio L, Orain M, Bouchard N, Muanza T, Rybicki FJ, Kafi K, Huntsman D, Joubert P, Chandelier F, Routy B. Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study. Front Oncol 2023; 13:1196414. [PMID: 37546399 PMCID: PMC10400292 DOI: 10.3389/fonc.2023.1196414] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
Background Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.
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Affiliation(s)
- Marion Tonneau
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
- Université de Médecine, Lille, France
| | - Kim Phan
- Imagia Canexia Health, Montreal, QC, Canada
| | - Venkata S. K. Manem
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
- Department of Mathematics and Computer Science, University of Quebec at Trois-Rivières, Trois-Rivières, QC, Canada
| | | | | | - Suzanne Kazandjian
- Department of Medical Oncology, Jewish General Hospital, Montreal, QC, Canada
| | - Davy Vanderweyen
- Department of Radiology, Centre Hospitalier de Sherbrooke (CHUS), Sherbrooke, QC, Canada
| | - Justin Panasci
- Department of Medical Oncology, Jewish General Hospital, Montreal, QC, Canada
| | - Julie Malo
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
| | - François Coulombe
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
| | - Andréanne Gagné
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
| | - Arielle Elkrief
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
- Hemato-Oncology Division, Centre Hospitalier de l’université de Montreal, Montreal, QC, Canada
| | - Wiam Belkaïd
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
| | | | - Michele Orain
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
| | - Nicole Bouchard
- Department of Oncology, Centre Hospitalier de Sherbrooke (CHUS), Sherbrooke, QC, Canada
| | - Thierry Muanza
- Department of Medical Oncology, Jewish General Hospital, Montreal, QC, Canada
- Department of Radiation Oncology, Lady Davis Institute of the Jewish General Hospital, Montreal, QC, Canada
| | | | - Kam Kafi
- Imagia Canexia Health, Montreal, QC, Canada
| | | | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
- Department of Pathology, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | | | - Bertrand Routy
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
- Hemato-Oncology Division, Centre Hospitalier de l’université de Montreal, Montreal, QC, Canada
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Carmo D, Ribeiro J, Dertkigil S, Appenzeller S, Lotufo R, Rittner L. A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images. Yearb Med Inform 2022; 31:277-295. [PMID: 36463886 PMCID: PMC9719778 DOI: 10.1055/s-0042-1742517] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
OBJECTIVES Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. METHODS We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. RESULTS We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. CONCLUSIONS We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.
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Affiliation(s)
- Diedre Carmo
- School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - Jean Ribeiro
- School of Electrical and Computer Engineering, University of Campinas, Brazil
| | | | | | - Roberto Lotufo
- School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Brazil,Correspondence to: Leticia Rittner Av. Albert Einstein, 400, Cidade Universitária Zeferino Vaz, Barão Geraldo - Campinas - SP 13083-852Brazil
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Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet. Med Biol Eng Comput 2022; 60:3311-3323. [DOI: 10.1007/s11517-022-02667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/12/2022] [Indexed: 11/25/2022]
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Zhang G, Yang Z, Huo B, Chai S, Jiang S. Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106419. [PMID: 34563895 DOI: 10.1016/j.cmpb.2021.106419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurately and reliably defining organs at risk (OARs) and tumors are the cornerstone of radiation therapy (RT) treatment planning for lung cancer. Almost all segmentation networks based on deep learning techniques rely on fully annotated data with strong supervision. However, existing public imaging datasets encountered in the RT domain frequently include singly labelled tumors or partially labelled organs because annotating full OARs and tumors in CT images is both rigorous and tedious. To utilize labelled data from different sources, we proposed a dual-path semi-supervised conditional nnU-Net for OARs and tumor segmentation that is trained on a union of partially labelled datasets. METHODS The framework employs the nnU-Net as the base model and introduces a conditioning strategy by incorporating auxiliary information as an additional input layer into the decoder. The conditional nnU-Net efficiently leverages prior conditional information to classify the target class at the pixelwise level. Specifically, we employ the uncertainty-aware mean teacher (UA-MT) framework to assist in OARs segmentation, which can effectively leverage unlabelled data (images from a tumor labelled dataset) by encouraging consistent predictions of the same input under different perturbations. Furthermore, we individually design different combinations of loss functions to optimize the segmentation of OARs (Dice loss and cross-entropy loss) and tumors (Dice loss and focal loss) in a dual path. RESULTS The proposed method is evaluated on two publicly available datasets of the spinal cord, left and right lung, heart, esophagus, and lung tumor, in which satisfactory segmentation performance has been achieved in term of both the region-based Dice similarity coefficient (DSC) and the boundary-based Hausdorff distance (HD). CONCLUSIONS The proposed semi-supervised conditional nnU-Net breaks down the barriers between nonoverlapping labelled datasets and further alleviates the problem of "data hunger" and "data waste" in multi-class segmentation. The method has the potential to help radiologists with RT treatment planning in clinical practice.
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Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Bin Huo
- Department of Oncology, Tianjin Medical University Second Hospital, Tianjin, 300211, China
| | - Shude Chai
- Department of Oncology, Tianjin Medical University Second Hospital, Tianjin, 300211, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China.
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Abstract
A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.
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Sahu P, Zhao Y, Bhatia P, Bogoni L, Jerebko A, Qin H. Structure Correction for Robust Volume Segmentation in Presence of Tumors. IEEE J Biomed Health Inform 2021; 25:1151-1162. [PMID: 32750948 DOI: 10.1109/jbhi.2020.3004296] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
CNN based lung segmentation models in absence of diverse training dataset fail to segment lung volumes in presence of severe pathologies such as large masses, scars, and tumors. To rectify this problem, we propose a multi-stage algorithm for lung volume segmentation from CT scans. The algorithm uses a 3D CNN in the first stage to obtain a coarse segmentation of the left and right lungs. In the second stage, shape correction is performed on the segmentation mask using a 3D structure correction CNN. A novel data augmentation strategy is adopted to train a 3D CNN which helps in incorporating global shape prior. Finally, the shape corrected segmentation mask is up-sampled and refined using a parallel flood-fill operation. The proposed multi-stage algorithm is robust in the presence of large nodules/tumors and does not require labeled segmentation masks for entire pathological lung volume for training. Through extensive experiments conducted on publicly available datasets such as NSCLC, LUNA, and LOLA11 we demonstrate that the proposed approach improves the recall of large juxtapleural tumor voxels by at least 15% over state-of-the-art models without sacrificing segmentation accuracy in case of normal lungs. The proposed method also meets the requirement of CAD software by performing segmentation within 5 seconds which is significantly faster than present methods.
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Cao H, Liu H, Song E, Hung CC, Ma G, Xu X, Jin R, Lu J. Dual-branch residual network for lung nodule segmentation. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105934] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Liu H, Cao H, Song E, Ma G, Xu X, Jin R, Jin Y, Hung CC. A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Phys Med 2019; 63:112-121. [PMID: 31221402 DOI: 10.1016/j.ejmp.2019.06.003] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 06/04/2019] [Accepted: 06/07/2019] [Indexed: 11/30/2022] Open
Abstract
It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the segmentation of lung nodules in the CT images. Our approach incorporates the multi-view and multi-scale features of different nodules from CT images. The proposed residual block based dual-path network extracts local features and rich contextual information of lung nodules. In addition, we designed an improved weighted sampling strategy to select training samples based on the edge. The proposed method was extensively evaluated on an LIDC dataset, which contains 986 nodules. Experimental results show that the CDP-ResNet achieves superior segmentation performance with an average DICE score (standard deviation) of 81.58% (11.05) on the LIDC dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison shows that the CDP-ResNet is slightly better than human experts in terms of segmentation accuracy. Meanwhile, the proposed segmentation method outperforms existing methods.
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Affiliation(s)
- Hong Liu
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
| | - Haichao Cao
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
| | - Enmin Song
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China.
| | - Guangzhi Ma
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
| | - Xiangyang Xu
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
| | - Renchao Jin
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
| | - Yong Jin
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
| | - Chih-Cheng Hung
- Huazhong University of Science and Technology, School of Computer Science & Technology, Wuhan 430074, China
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Chung H, Ko H, Jeon SJ, Yoon KH, Lee J. Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:1800513. [PMID: 29910995 PMCID: PMC6001848 DOI: 10.1109/jtehm.2018.2837901] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 03/28/2018] [Accepted: 05/06/2018] [Indexed: 11/07/2022]
Abstract
OBJECTIVE chest computed tomography (CT) images and their quantitative analyses have become increasingly important for a variety of purposes, including lung parenchyma density analysis, airway analysis, diaphragm mechanics analysis, and nodule detection for cancer screening. Lung segmentation is an important prerequisite step for automatic image analysis. We propose a novel lung segmentation method to minimize the juxta-pleural nodule issue, a notorious challenge in the applications. METHOD we initially used the Chan-Vese (CV) model for active lung contours and adopted a Bayesian approach based on the CV model results, which predicts the lung image based on the segmented lung contour in the previous frame image or neighboring upper frame image. Among the resultant juxta-pleural nodule candidates, false positives were eliminated through concave points detection and circle/ellipse Hough transform. Finally, the lung contour was modified by adding the final nodule candidates to the area of the CV model results. RESULTS to evaluate the proposed method, we collected chest CT digital imaging and communications in medicine images of 84 anonymous subjects, including 42 subjects with juxta-pleural nodules. There were 16 873 images in total. Among the images, 314 included juxta-pleural nodules. Our method exhibited a disc similarity coefficient of 0.9809, modified hausdorff distance of 0.4806, sensitivity of 0.9785, specificity of 0.9981, accuracy of 0.9964, and juxta-pleural nodule detection rate of 96%. It outperformed existing methods, such as the CV model used alone, the normalized CV model, and the snake algorithm. Clinical impact: the high accuracy with the juxta-pleural nodule detection in the lung segmentation can be beneficial for any computer aided diagnosis system that uses lung segmentation as an initial step.
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Affiliation(s)
- Heewon Chung
- Department of Biomedical EngineeringWonkwang University College of MedicineIksan54538South Korea
| | - Hoon Ko
- Department of Biomedical EngineeringWonkwang University College of MedicineIksan54538South Korea
| | - Se Jeong Jeon
- Department of RadiologyWonkwang University College of MedicineIksan54538South Korea
| | - Kwon-Ha Yoon
- Department of RadiologyWonkwang University College of MedicineIksan54538South Korea
| | - Jinseok Lee
- Department of Biomedical EngineeringWonkwang University College of MedicineIksan54538South Korea
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Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol 2017; 126:312-317. [PMID: 29208513 DOI: 10.1016/j.radonc.2017.11.012] [Citation(s) in RCA: 232] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 11/16/2017] [Accepted: 11/21/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE Contouring of organs at risk (OARs) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. MATERIAL AND METHODS Twenty CT scans of stage I-III NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded. RESULTS With a median time of 20 min for manual contouring, the total median time saved was 7.8 min when using atlas-based contouring and 10 min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring. CONCLUSIONS User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions.
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