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Hlouschek J, König B, Bos D, Santiago A, Zensen S, Haubold J, Pöttgen C, Herz A, Opitz M, Wetter A, Guberina M, Stuschke M, Zylka W, Kühl H, Guberina N. Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study. Diagnostics (Basel) 2023; 14:28. [PMID: 38201337 PMCID: PMC10804383 DOI: 10.3390/diagnostics14010028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/10/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVis) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function. Furthermore, a deep-learning algorithm for automatic contouring was applied (IV). An intermodality comparison of the above-mentioned strategies for contouring GTVis was performed. For the mean GTVref (standard deviation (SD)), the interquartile range (IQR)) was 0.68 mL (0.33; 0.34-1.1). GTV segmentation was distributed as follows: (I) 0.61 mL (0.27; 0.36-0.92); (II) 0.41 mL (0.28; 0.23-0.63); (III) 0.65 mL (0.35; 0.32-0.90); and (IV) 0.61 mL (0.29; 0.33-0.95). GTVref was found to be significantly correlated with GTVis (I) p < 0.001, r = 0.989 (III) p = 0.001, r = 0.916, and (IV) p < 0.001, r = 0.986, but not with (II) p = 0.091, r = 0.595. The Sørensen-Dice indices for the semi-automatic tools were 0.74 (I), 0.57 (II) and 0.71 (III). For the semi-automatic, conventional segmentation tools evaluated, the adaptive-brush function (I) performed closest to the reference standard (0). The automatic deep learning tool (IV) showed high performance for auto-segmentation and was close to the reference standard. For high precision radiation therapy, visual control, and, where necessary, manual correction, are mandatory for all evaluated tools.
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Affiliation(s)
- Julian Hlouschek
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Britta König
- Department of Radiology, University Hospital Muenster (UKM), Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Muenster, Germany
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Alina Santiago
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Christoph Pöttgen
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Andreas Herz
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Marcel Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Axel Wetter
- Department of Diagnostic and Interventional Radiology, Neuroradiology, Asklepios Klinikum Harburg, Eißendorfer Pferdeweg 52, 21075 Hamburg, Germany
| | - Maja Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Martin Stuschke
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Waldemar Zylka
- Westphalian University, Campus Gelsenkirchen, Neidenburger Str. 43, 45897 Gelsenkirchen, Germany
| | - Hilmar Kühl
- Department of Radiology, St. Bernhard-Hospital Kamp-Lintfort, Bürgermeister-Schmelzing-Str. 90, 47475 Kamp-Lintfort, Germany
| | - Nika Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
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Du G, Zhang P, Guo J, Pang X, Kan G, Zeng B, Chen X, Liang J, Zhan Y. MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network. J Digit Imaging 2023; 36:2411-2426. [PMID: 37714969 PMCID: PMC10584774 DOI: 10.1007/s10278-023-00890-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 09/17/2023] Open
Abstract
Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate identification and segmentation of muscle fiber boundary is an important prerequisite for the evaluation of skeletal muscle fiber atrophy. However, there are many challenges to segment muscle fiber from immunofluorescence images, including the presence of low contrast in fiber boundaries in immunofluorescence images and the influence of background noise. Due to the limitations of traditional convolutional neural network-based segmentation methods in capturing global information, they cannot achieve ideal segmentation results. In this paper, we propose a muscle fiber segmentation network (MF-Net) method for effective segmentation of macaque muscle fibers in immunofluorescence images. The network adopts a dual encoder branch composed of convolutional neural networks and transformer to effectively capture local and global feature information in the immunofluorescence image, highlight foreground features, and suppress irrelevant background noise. In addition, a low-level feature decoder module is proposed to capture more global context information by combining different image scales to supplement the missing detail pixels. In this study, a comprehensive experiment was carried out on the immunofluorescence datasets of six macaques' weightlessness models and compared with the state-of-the-art deep learning model. It is proved from five segmentation indices that the proposed automatic segmentation method can be accurately and effectively applied to muscle fiber segmentation in shank immunofluorescence images.
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Affiliation(s)
| | - Peng Zhang
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Jianzhong Guo
- Institute of Applied Acoustics, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Xiangsheng Pang
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Guanghan Kan
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Bin Zeng
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Xiaoping Chen
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China.
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, 710071, China.
| | - Yonghua Zhan
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China.
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Guedes Pinto E, Penha D, Ravara S, Monaghan C, Hochhegger B, Marchiori E, Taborda-Barata L, Irion K. Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis. Insights Imaging 2023; 14:152. [PMID: 37741928 PMCID: PMC10517915 DOI: 10.1186/s13244-023-01480-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/08/2023] [Indexed: 09/25/2023] Open
Abstract
Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.
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Affiliation(s)
- Erique Guedes Pinto
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal.
| | - Diana Penha
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | - Sofia Ravara
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Colin Monaghan
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | | | - Edson Marchiori
- Faculdade de Medicina, Universidade Federal Do Rio de Janeiro, Bloco K - Av. Carlos Chagas Filho, 373 - 2º Andar, Sala 49 - Cidade Universitária da Universidade Federal Do Rio de Janeiro, Rio de Janeiro - RJ, 21044-020, Brasil
- Faculdade de Medicina, Universidade Federal Fluminense, Av. Marquês Do Paraná, 303 - Centro, Niterói - RJ, 24220-000, Brasil
| | - Luís Taborda-Barata
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Klaus Irion
- Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Oxford Rd, Manchester, M13 9WL, UK
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Tong N, Xu Y, Zhang J, Gou S, Li M. Robust and efficient abdominal CT segmentation using shape constrained multi-scale attention network. Phys Med 2023; 110:102595. [PMID: 37178624 DOI: 10.1016/j.ejmp.2023.102595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/02/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
PURPOSE Although many deep learning-based abdominal multi-organ segmentation networks have been proposed, the various intensity distributions and organ shapes of the CT images from multi-center, multi-phase with various diseases introduce new challenges for robust abdominal CT segmentation. To achieve robust and efficient abdominal multi-organ segmentation, a new two-stage method is presented in this study. METHODS A binary segmentation network is used for coarse localization, followed by a multi-scale attention network for the fine segmentation of liver, kidney, spleen, and pancreas. To constrain the organ shapes produced by the fine segmentation network, an additional network is pre-trained to learn the shape features of the organs with serious diseases and then employed to constrain the training of the fine segmentation network. RESULTS The performance of the presented segmentation method was extensively evaluated on the multi-center data set from the Fast and Low GPU Memory Abdominal oRgan sEgmentation (FLARE) challenge, which was held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) were calculated to quantitatively evaluate the segmentation accuracy and efficiency. An average DSC and NSD of 83.7% and 64.4% were achieved, and our method finally won the second place among more than 90 participating teams. CONCLUSIONS The evaluation results on the public challenge demonstrate that our method shows promising performance in robustness and efficiency, which may promote the clinical application of the automatic abdominal multi-organ segmentation.
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Affiliation(s)
- Nuo Tong
- AI-based Big Medical Imaging Data Frontier Research Center, Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yinan Xu
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Jinsong Zhang
- Xijing Hospital of Air Force Military Medical University, Xian, Shaanxi 710032, China
| | - Shuiping Gou
- AI-based Big Medical Imaging Data Frontier Research Center, Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, Shaanxi 710071, China; Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
| | - Mengbin Li
- Xijing Hospital of Air Force Military Medical University, Xian, Shaanxi 710032, China.
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Modak S, Abdel-Raheem E, Rueda L. Applications of Deep Learning in Disease Diagnosis of Chest Radiographs: A Survey on Materials and Methods. BIOMEDICAL ENGINEERING ADVANCES 2023. [DOI: 10.1016/j.bea.2023.100076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
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Zhao W, Liu J. Artificial intelligence in lung cancer: Application and future thinking. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:994-1000. [PMID: 36097766 PMCID: PMC10950116 DOI: 10.11817/j.issn.1672-7347.2022.210645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Indexed: 06/15/2023]
Abstract
The availability of medical big data and the rapid development of computer software and hardware have greatly promoted the advancement of intelligent medical healthcare. Artificial intelligence (AI) has been successfully applied in many fields of medicine, especially in lung cancer. The performance of AI in some specific tasks has surpassed that of humans. Several AI software has been deeply used in clinical practice to help decision-making, which is producing a profound influence on clinicians. At present, the application of AI in the field of lung cancer mainly includes detection, segmentation, classification, prognosis prediction, efficacy evaluation, and so on. AI faces certain challenges and opportunities in the era of big data in terms of data acquisition, annotation and interpretability. Researchers have conducted deep and extensive studies using AI in the field of lung cancer, and AI is expected to become a powerful assistant in the prevention and treatment of lung cancer. AI is bringing an unprecedented revolution to radiologists, but the role of radiologists is crucial in the development of AI.
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Affiliation(s)
- Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China.
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China.
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Moragheb MA, Badie A, Noshad A. An Effective Approach for Automated Lung Node Detection using CT Scans. J Biomed Phys Eng 2022; 12:377-386. [PMID: 36059280 PMCID: PMC9395629 DOI: 10.31661/jbpe.v0i0.2110-1412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures. OBJECTIVE This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. MATERIAL AND METHODS In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. RESULTS The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). CONCLUSION Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.
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Affiliation(s)
- Mohammad Amin Moragheb
- MSc, Department of Computer Engineering, Faculty of Engineering, Mamasani Higher Education Center, Mamasani, Iran
| | - Ali Badie
- MSc, Department of Computer Engineering, Faculty of Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
| | - Ali Noshad
- BSc, Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
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