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Chiwhane S, Shrotriya L, Dhumane A, Kothari S, Dharrao D, Bagane P. Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques. MethodsX 2024; 12:102692. [PMID: 38638453 PMCID: PMC11024647 DOI: 10.1016/j.mex.2024.102692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/02/2024] [Indexed: 04/20/2024] Open
Abstract
With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays. The proposed method is tested against other pneumothorax segmentation techniques using a dataset of 'chest X-rays' with 'pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.
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Affiliation(s)
- Shwetambari Chiwhane
- Symbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India
| | - Lalit Shrotriya
- Symbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India
| | - Amol Dhumane
- Symbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India
| | - Sonali Kothari
- Symbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India
| | - Deepak Dharrao
- Symbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India
| | - Pooja Bagane
- Symbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India
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2
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Veras Magalhães G, L. de S. Santos R, H. S. Vogado L, Cardoso de Paiva A, de Alcântara dos Santos Neto P. XRaySwinGen: Automatic medical reporting for X-ray exams with multimodal model. Heliyon 2024; 10:e27516. [PMID: 38560155 PMCID: PMC10979158 DOI: 10.1016/j.heliyon.2024.e27516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/04/2024] Open
Abstract
The importance of radiology in modern medicine is acknowledged for its non-invasive diagnostic capabilities, yet the manual formulation of unstructured medical reports poses time constraints and error risks. This study addresses the common limitation of Artificial Intelligence applications in medical image captioning, which typically focus on classification problems, lacking detailed information about the patient's condition. Despite advancements in AI-generated medical reports that incorporate descriptive details from X-ray images, which are essential for comprehensive reports, the challenge persists. The proposed solution involves a multimodal model utilizing Computer Vision for image representation and Natural Language Processing for textual report generation. A notable contribution is the innovative use of the Swin Transformer as the image encoder, enabling hierarchical mapping and enhanced model perception without a surge in parameters or computational costs. The model incorporates GPT-2 as the textual decoder, integrating cross-attention layers and bilingual training with datasets in Portuguese PT-BR and English. Promising results are noted in the proposed database with ROUGE-L 0.748, METEOR 0.741, and NIH CHEST X-ray with ROUGE-L 0.404 and METEOR 0.393.
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Affiliation(s)
| | | | - Luis H. S. Vogado
- Departamento de Computação, Universidade Federal do Piauí, Teresina, Brazil
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3
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Malik H, Anees T. Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PLoS One 2024; 19:e0296352. [PMID: 38470893 DOI: 10.1371/journal.pone.0296352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 03/14/2024] Open
Abstract
Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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4
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Chutia U, Tewari AS, Singh JP, Raj VK. Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01005-0. [PMID: 38467955 DOI: 10.1007/s10278-024-01005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 03/13/2024]
Abstract
Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes.
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Affiliation(s)
- Upasana Chutia
- Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India
| | - Anand Shanker Tewari
- Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India
| | - Jyoti Prakash Singh
- Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India.
| | - Vikash Kumar Raj
- National Institute of Technology Patna, Patna, 800005, Bihar, India
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Katzman BD, Alabousi M, Islam N, Zha N, Patlas MN. Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis. Can Assoc Radiol J 2024:8465371231220885. [PMID: 38189265 DOI: 10.1177/08465371231220885] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Pneumothorax is a common acute presentation in healthcare settings. A chest radiograph (CXR) is often necessary to make the diagnosis, and minimizing the time between presentation and diagnosis is critical to deliver optimal treatment. Deep learning (DL) algorithms have been developed to rapidly identify pathologic findings on various imaging modalities. PURPOSE The purpose of this systematic review and meta-analysis was to evaluate the overall performance of studies utilizing DL algorithms to detect pneumothorax on CXR. METHODS A study protocol was created and registered a priori (PROSPERO CRD42023391375). The search strategy included studies published up until January 10, 2023. Inclusion criteria were studies that used adult patients, utilized computer-aided detection of pneumothorax on CXR, dataset was evaluated by a qualified physician, and sufficient data was present to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Bivariate random effects meta-analyses and meta-regression modeling were performed. RESULTS Twenty-three studies were selected, including 34 011 patients and 34 075 CXRs. The pooled sensitivity and specificity were 87% (95% confidence interval, 81%, 92%) and 95% (95% confidence interval, 92%, 97%), respectively. The study design, use of an institutional/public data set and risk of bias had no significant effect on the sensitivity and specificity of pneumothorax detection. CONCLUSIONS The relatively high sensitivity and specificity of pneumothorax detection by deep-learning showcases the vast potential for implementation in clinical settings to both augment the workflow of radiologists and assist in more rapid diagnoses and subsequent patient treatment.
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Affiliation(s)
- Benjamin D Katzman
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Mostafa Alabousi
- Department of Medical Imaging, McMaster University, Hamilton, ON, Canada
| | - Nabil Islam
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Nanxi Zha
- Department of Medical Imaging, McMaster University, Hamilton, ON, Canada
| | - Michael N Patlas
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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Su Z, Rezapour M, Sajjad U, Gurcan MN, Niazi MKK. Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images. Comput Biol Med 2023; 167:107607. [PMID: 37890421 PMCID: PMC10699124 DOI: 10.1016/j.compbiomed.2023.107607] [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: 05/19/2023] [Revised: 09/18/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023]
Abstract
Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. We introduce a novel representation learning for histopathology images to identify representative normal keys. These keys facilitate the selection of salient instances within WSIs, forming bags with high tumor-to-normal ratios. Finally, an attention mechanism is employed for slide-level classification based on formed bags. Our results show that salient instance inference can improve the tumor-to-normal area ratio in the tumor WSIs. As a result, SiiMIL achieves 0.9225 AUC and 0.7551 recall on the Camelyon16 dataset, which outperforms the existing MIL models. In addition, SiiMIL can generate tumor-sensitive attention heatmaps that is more interpretable to pathologists than the widely used attention-based MIL method. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times.
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Affiliation(s)
- Ziyu Su
- Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston-Salem, 27104, USA.
| | - Mostafa Rezapour
- Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston-Salem, 27104, USA
| | - Usama Sajjad
- Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston-Salem, 27104, USA
| | - Metin Nafi Gurcan
- Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston-Salem, 27104, USA
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7
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Liu Y, Liang P, Liang K, Chang Q. Automatic and efficient pneumothorax segmentation from CT images using EFA-Net with feature alignment function. Sci Rep 2023; 13:15291. [PMID: 37714871 PMCID: PMC10504271 DOI: 10.1038/s41598-023-42388-4] [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: 05/10/2023] [Accepted: 09/09/2023] [Indexed: 09/17/2023] Open
Abstract
Pneumothorax is a condition involving a collapsed lung, which requires accurate segmentation of computed tomography (CT) images for effective clinical decision-making. Numerous convolutional neural network-based methods for medical image segmentation have been proposed, but they often struggle to balance model complexity with performance. To address this, we introduce the Efficient Feature Alignment Network (EFA-Net), a novel medical image segmentation network designed specifically for pneumothorax CT segmentation. EFA-Net uses EfficientNet as an encoder to extract features and a Feature Alignment (FA) module as a decoder to align features in both the spatial and channel dimensions. This design allows EFA-Net to achieve superior segmentation performance with reduced model complexity. In our dataset, our method outperforms various state-of-the-art methods in terms of accuracy and efficiency, achieving a Dice coefficient of 90.03%, an Intersection over Union (IOU) of 81.80%, and a sensitivity of 88.94%. Notably, EFA-Net has significantly lower FLOPs (1.549G) and parameters (0.432M), offering better robustness and facilitating easier deployment. Future work will explore the integration of downstream applications to enhance EFA-Net's utility for clinicians and patients in real-world diagnostic scenarios. The source code of EFA-Net is available at: https://github.com/tianjiamutangchun/EFA-Net .
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Affiliation(s)
- Yinghao Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
- Shanghai University of Medicine and Health Sciences, Shanghai, 200237, China
- Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Pengchen Liang
- School of Microelectronics, Shanghai University, Shanghai, 201800, China
| | - Kaiyi Liang
- Department of Radiology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Key Laboratory of Shanghai Municipal Health Commission for Smart Image, Shanghai, 201800, China.
| | - Qing Chang
- Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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8
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Sugibayashi T, Walston SL, Matsumoto T, Mitsuyama Y, Miki Y, Ueda D. Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis. Eur Respir Rev 2023; 32:32/168/220259. [PMID: 37286217 DOI: 10.1183/16000617.0259-2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/16/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed. METHODS A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis via a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool. RESULTS In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96-0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79-89%) for DL and 85% (95% CI 73-92%) for physicians and the pooled specificity was 96% (95% CI 94-98%) for DL and 98% (95% CI 95-99%) for physicians. More than half of the original studies (57%) had a high risk of bias. CONCLUSIONS Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.
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Affiliation(s)
- Takahiro Sugibayashi
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Shannon L Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Toshimasa Matsumoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan
| | - Yasuhito Mitsuyama
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan
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Irmici G, Cè M, Caloro E, Khenkina N, Della Pepa G, Ascenti V, Martinenghi C, Papa S, Oliva G, Cellina M. Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? Diagnostics (Basel) 2023; 13:diagnostics13020216. [PMID: 36673027 PMCID: PMC9858224 DOI: 10.3390/diagnostics13020216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
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Affiliation(s)
- Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natallia Khenkina
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Giancarlo Oliva
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
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10
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Siriapisith T, Kusakunniran W, Haddawy P. A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm. PeerJ Comput Sci 2022; 8:e1033. [PMID: 35875647 PMCID: PMC9299237 DOI: 10.7717/peerj-cs.1033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSV-UNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively.
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Affiliation(s)
- Thanongchai Siriapisith
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
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11
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Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images. Diagnostics (Basel) 2022; 12:diagnostics12040915. [PMID: 35453963 PMCID: PMC9025806 DOI: 10.3390/diagnostics12040915] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 12/04/2022] Open
Abstract
Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
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