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Ahmadi A, Courtney M, Ren C, Ingalls B. A benchmarked comparison of software packages for time-lapse image processing of monolayer bacterial population dynamics. Microbiol Spectr 2024; 12:e0003224. [PMID: 38980028 PMCID: PMC11302142 DOI: 10.1128/spectrum.00032-24] [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: 01/04/2024] [Accepted: 04/26/2024] [Indexed: 07/10/2024] Open
Abstract
Time-lapse microscopy offers a powerful approach for analyzing cellular activity. In particular, this technique is valuable for assessing the behavior of bacterial populations, which can exhibit growth and intercellular interactions in a monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several image-processing software packages have been developed to facilitate analysis. It can thus be a challenge to identify the software package best suited to a particular research goal. Here, we compare four software packages that support the analysis of 2D time-lapse images of cellular populations: CellProfiler, SuperSegger-Omnipose, DeLTA, and FAST. We compare their performance against benchmarked results on time-lapse observations of Escherichia coli populations. Performance varies across the packages, with each of the four outperforming the others in at least one aspect of the analysis. Not surprisingly, the packages that have been in development for longer showed the strongest performance. We found that deep learning-based approaches to object segmentation outperformed traditional approaches, but the opposite was true for frame-to-frame object tracking. We offer these comparisons, together with insight into usability, computational efficiency, and feature availability, as a guide to researchers seeking image-processing solutions. IMPORTANCE Time-lapse microscopy provides a detailed window into the world of bacterial behavior. However, the vast amount of data produced by these techniques is difficult to analyze manually. We have analyzed four software tools designed to process such data and compared their performance, using populations of commonly studied bacterial species as our test subjects. Our findings offer a roadmap to scientists, helping them choose the right tool for their research. This comparison bridges a gap between microbiology and computational analysis, streamlining research efforts.
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Affiliation(s)
- Atiyeh Ahmadi
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
| | - Matthew Courtney
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Carolyn Ren
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Brian Ingalls
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
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Wang W, Sheng R, Liao S, Wu Z, Wang L, Liu C, Yang C, Jiang R. LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01172-0. [PMID: 38940888 DOI: 10.1007/s10278-024-01172-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 06/29/2024]
Abstract
Postoperative complications of radical gastrectomy seriously affect postoperative recovery and require accurate risk prediction. Therefore, this study aimed to develop a prediction model specifically tailored to guide perioperative clinical decision-making for postoperative complications in patients with gastric cancer. A retrospective analysis was conducted on patients who underwent radical gastrectomy at the First Affiliated Hospital of Nanjing Medical University between April 2022 and June 2023. A total of 166 patients were enrolled. Patient demographic characteristics, laboratory examination results, and surgical pathological features were recorded. Preoperative abdominal CT scans were used to segment the visceral fat region of the patients through 3Dslicer, a 3D Convolutional Neural Network (3D-CNN) to extract image features and the LASSO regression model was employed for feature selection. Moreover, an ensemble learning strategy was adopted to train the features and predict postoperative complications of gastric cancer. The prediction performance of the LGBM (Light Gradient Boosting Machine), XGB (XGBoost), RF (Random Forest), and GBDT (Gradient Boosting Decision Tree) models was evaluated through fivefold cross-validation. This study successfully constructed a model for predicting early complications following radical gastrectomy based on the optimal algorithm, LGBM. The LGBM model yielded an AUC value of 0.9232 and an accuracy of 87.28% (95% CI, 75.61-98.95%), surpassing the performance of other models. Through ensemble learning and integration of perioperative clinical data and visceral fat radiomics, a predictive LGBM model was established. This model has the potential to facilitate individualized clinical decision-making and the early recovery of patients with gastric cancer post-surgery.
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Affiliation(s)
- Wenli Wang
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rongrong Sheng
- Information Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shumei Liao
- Information Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zifeng Wu
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Linjun Wang
- Department of Gastric Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Cunming Liu
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Chun Yang
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Riyue Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Hsieh J. Synthetization of high-dose images using low-dose CT scans. Med Phys 2024; 51:113-125. [PMID: 37975625 DOI: 10.1002/mp.16833] [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: 01/11/2023] [Revised: 09/05/2023] [Accepted: 10/25/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Radiation dose reduction has been the focus of many research activities in x-ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients. PURPOSE Reconstruction algorithm-based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high-dose images (SHD) from the data acquired with low-dose scans. A representative example is the model-based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high-quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low-dose clinical datasets that overcomes both the noise texture issue and the data availability issue. METHODS Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick-slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD. RESULTS The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise-texture. CONCLUSIONS We present a method to generate SHD datasets from regularly acquired low-dose CT scans. Images produced with the proposed approach exhibit excellent noise-reduction with the desired noise-texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined.
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Affiliation(s)
- Jiang Hsieh
- Independent Consultant, Brookfield, Wisconsin, USA
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Nur-A-Alam M, Nasir MK, Ahsan M, Based MA, Haider J, Kowalski M. Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN. Sci Rep 2023; 13:20063. [PMID: 37973820 PMCID: PMC10654719 DOI: 10.1038/s41598-023-47183-9] [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: 03/06/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023] Open
Abstract
The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.
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Affiliation(s)
- Md Nur-A-Alam
- Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mostofa Kamal Nasir
- Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, York, YO10 5GH, UK
| | - Md Abdul Based
- Department of Computer Science & Engineering, Dhaka International University, Dhaka, 1205, Bangladesh
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester, M1 5GD, UK
| | - Marcin Kowalski
- Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, Poland.
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Balogh ZA, Janos Kis B. Comparison of CT noise reduction performances with deep learning-based, conventional, and combined denoising algorithms. Med Eng Phys 2022; 109:103897. [DOI: 10.1016/j.medengphy.2022.103897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 09/18/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022]
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Anam C, Naufal A, Fujibuchi T, Matsubara K, Dougherty G. Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images. J Appl Clin Med Phys 2022; 23:e13719. [PMID: 35808971 PMCID: PMC9512356 DOI: 10.1002/acm2.13719] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 12/25/2022] Open
Abstract
Purpose We have developed a software to automatically find the contrast–detail (C–D) curve based on the statistical low‐contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics. Methods A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2‐pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C–D curve was obtained and the target size at an MDC of 0.6% (i.e., 6‐HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer. Results The developed system was able to measure C–D curves from different phantoms and scanners. We found that the C–D curves follow a power‐law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low‐contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD. Conclusions The statistical LCD measurement method can generate a C–D curve automatically, quickly, and objectively.
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Affiliation(s)
- Choirul Anam
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Central Java, Indonesia
| | - Ariij Naufal
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Central Java, Indonesia
| | - Toshioh Fujibuchi
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kosuke Matsubara
- Department of Quantum Medical Technology, Faculty of Health Sciences, Institute of Medical Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Geoff Dougherty
- Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, California, USA
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Omer H, Tamam N, Alameen S, Algadi S, Thanh Tai D, Sulieman A. Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques. Saudi J Biol Sci 2022; 29:2180-2186. [PMID: 35531247 PMCID: PMC9073048 DOI: 10.1016/j.sjbs.2021.11.043] [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: 08/29/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 11/18/2022] Open
Abstract
Filters reduce the noise at the expense of visual image quality. Digital interpretation of images prevents misinterpretation of the images due to blurring of the images. Dose reduction without compromising the diagnostic.
Introduction Medical images are usually affected by biological and physical artifacts or noise, which reduces image quality and hence poses difficulties in visual analysis, interpretation and thus requires higher doses and increased radiographs repetition rate. Objectives This study aims at assessing image quality during CT abdomen and brain examinations using filtering techniques as well as estimating the radiogenic risk associated with CT abdomen and brain examinations. Materials and Methods The data were collected from the Radiology Department at Royal Care International (RCI) Hospital, Khartoum, Sudan. The study included 100 abdominal CT images and 100 brain CT images selected from adult patients. Filters applied are namely: Mean filter, Gaussian filter, Median filter and Minimum filter. In this study, image quality after denoising is measured based on the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM). Results The results show that the images quality parameters become higher after applications of filters. Median filter showed improved image quality as interpreted by the measured parameters: PSNR and SSIM, and it is thus considered as a better filter for removing the noise from all other applied filters. Discussion The noise removed by the different filters applied to the CT images resulted in enhancing high quality images thereby effectively revealing the important details of the images without increasing the patients’ risks from higher doses. Conclusions Filtering and image reconstruction techniques not only reduce the dose and thus the radiation risks, but also enhances high quality imaging which allows better diagnosis.
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Affiliation(s)
- Hiba Omer
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
| | - Nissren Tamam
- Physics Department, College of Sciences, Princess Nourah bint Abdulrahman University, P.O Box 84428, Riyadh 11671, Saudi Arabia
- Corresponding author.
| | - Suhaib Alameen
- Sudan University of Science and Technology, College of Medical Radiologic Science, P.O.Box 1908, Khartoum, Sudan
| | - Sahar Algadi
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
| | - Duong Thanh Tai
- Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh 749000, Vietnam
| | - Abdelmoneim Sulieman
- Prince Sattam Bin Abdulaziz University, College of Applied Medical Sciences, Radiology and Medical Imaging Department, PO Box 422, Alkharj 11942, Saudi Arabia
- Basic Science Department, College of Medical Radiologic Science, Sudan University of Science and Technology, P.O.Box 1908, Khartoum 11111, Sudan
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Yang J, Xiao C, Wen H, Sun K, Wu X, Feng X. Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8916076. [PMID: 35281950 PMCID: PMC8906978 DOI: 10.1155/2022/8916076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/22/2022] [Accepted: 01/26/2022] [Indexed: 11/21/2022]
Abstract
This work was to explore the efficacy of intelligent algorithm-based computed tomography (CT) to evaluate platelet-rich plasma (PRP) combined with vacuum sealing drainage (VSD) in the treatment of patients with pressure ulcers. Based on the u-net network structure, an image denoising algorithm based on double residual convolution neural network (Dr-CNN) was proposed to denoise the CT images. A total of 84 patients who were hospitalized in hospital were randomly divided into group A (without any intervention), group B (PRP treatment), group C (VSD treatment), and group D (PRP+VSD treatment). Procalcitonin (PCT) was detected by enzyme-linked immunosorbent assay (ELISA) combined with immunofluorescence method, C-reactive protein (CRP) was detected by rate reflectance turbidimetry (RRT), and interleukin-6 (IL-6) was detected by electrochemiluminescence method. The results showed that after treatment, 44 cases (52.38%) of pressure ulcers patients recovered, 24 cases (28.57%) had no change in stage, and 16 cases (19.04%) developed pressure ulcers. The pain scores of group D at 1 week (3.35 ± 0.56 points) and 2 weeks (2.76 ± 0.55 points) after treatment were significantly lower than those in group C (7.77 ± 0.58 points and 6.34 ± 0.44 points, respectively). The time of complete wound healing in group D (24.5 ± 2.32) was obviously lower in contrast to that in groups A, B, and C (35.54 ± 3.22 days, 30.23 ± 2 days, and 29.34 ± 2.15 days, respectively). In addition, the medical satisfaction of group D (8.74 ± 0.69) was significantly higher than that of groups A, B, and C (4.69 ± 0.85, 5.22 ± 0.31, and 5.18 ± 0.59, respectively). The levels of IL-6 and PCT in group D were lower than those in groups A, B, and C, and the differences were statistically significant (P < 0.01). The average values of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the Dr-CNN network model were 37.21 ± 1.09 dB and 0.925 ± 0.01, respectively, which were higher than other algorithms. The mean values of root mean square error (MSE) and normalized mean absolute distance (NMAD) of the Dr-CNN network model were 0.022 ± 0.002 and 0.126 ± 0.012, respectively, which were significantly lower than other algorithms (P < 0.05). The experimental results showed that PrP combined with VSD could significantly reduce the inflammatory response of patients with pressure ulcers. PRP combined with VSD could significantly reduce the pain of dressing change for patients. Moreover, the performance model of image denoising algorithm based on double residual convolutional neural network was better than other algorithms.
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Affiliation(s)
- Jingzhe Yang
- Department of Burn and Plastic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, 067000 Hebei, China
| | - Changshuan Xiao
- Department of Burn and Plastic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, 067000 Hebei, China
| | - Hailing Wen
- Department of Burn and Plastic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, 067000 Hebei, China
| | - Kui Sun
- Department of Burn and Plastic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, 067000 Hebei, China
| | - Xiaoming Wu
- Department of Burn and Plastic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, 067000 Hebei, China
| | - Xinshu Feng
- Department of Burn and Plastic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, 067000 Hebei, China
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