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Conference on Lasers in Medicine and Dentistry - Diagnostics and Treatment

作者： Xie, SS Zheng, W Li, YS Lai, KZ Lin, QR FUJIAN NORMAL UNIV INST LASERFUZHOU 350007PEOPLES R CHINA

ISBN: (纸本)0819422886

Autofluorescence of the tumor and surrounding tissue is the largest background source in fluorescence diagnosis. A new system which applied computer image processing technique to lung cancer localization by laser fluorescence bronchoscopy has been developed to subtract the autofluorescence background. The results of our trial tests in tissue- simulating phantom and the porcine thigh muscle models are satisfied. There is great hope that this computer-assisted image processing system will significantly enhance the contrast of fluorescence image and drop false results for early human lung cancer examination.

关键词： laser-induced fluorescence computer-assisted image processing lung cancer localization autofluorescence Hematoporphyrin Derivative (HpD)

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An objective method to measure cell survival by computer-assisted image processing of numeric images of Petri dishes

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PHYSICS IN MEDICINE AND BIOLOGY 2003年第11期48卷 1551-1563页

作者： Biston, MC Corde, S Camus, E Marti-Battle, R Estève, F Balosso, J CHU Grenoble Unite IRM Equipe Accueil 2941 F-38043 Grenoble 09 France Samba Technol ZIRST 53 F-38240 Meylan France

This work establishes an objective method to measure cell clonogenic survival by computer-assisted image processing using images of cell cultures fixed and stained in Petri dishes. The procedure, developed by Samba Technologies, consists of acquiring Petri dish pictures with a desktop scanner and analysing them by computer, using algorithms based on the 'top hat' filter. The results from the automated count for the cell line SQ20B are compared with those found by two observers, before and after normalization of the counting. After normalization, the shape of the survival curves of the 'manual' counting of the Petri dishes shows a good correlation between both observers. The software enables the small visible differences in count between observers to be eliminated. The comparison between the absolute number of colonies shows an increased difference between the two manual scorings that can be as great as 67 colonies, whereas the difference between the two automated counts is never greater than 8 colonies. These results demonstrate that the 'manual' count is inter- and intraobserver variable, whereas the automatic count performs reproducible cell colony counts, thereby minimizing user-generated bias. The large amount of data produced also gives information about cell and colony characteristics. Thus, this computer-assisted method has considerably improved the reliability of our statistical results.

关键词： absolute number cell viability manual count image processing Cell Survival desktop scanners top hats Petri dishes Observers normalization colonies computer-assisted image processing

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IMMUNOHISTOCHEMICAL ANALYSIS OF THE TOPOGRAPHICAL RELATIONSHIP BETWEEN THE ESTROGEN-RECEPTOR AND PROGESTERONE-RECEPTOR IN 5 HUMAN-BREAST CANCERS - A SIMULTANEOUS DEMONSTRATION OF BOTH NUCLEAR RECEPTORS IN THE SAME SECTION BY USING A computer-assisted image-processing

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VIRCHOWS ARCHIV A-PATHOLOGICAL ANATOMY AND HISTOPATHOLOGY 1989年第4期414卷 339-344页

作者： BONKHOFF, H WERNERT, N 1. Institute of Pathology Department of General and Special Pathology University of the Saarland D-6650 Homburg/Saar Federal Republic of Germany

A technique is described which allows precise assessment of the topographical relationship between the estrogen receptor (ER) and the progesterone receptor (PR) in the same histological section. It is based on the analysis of the results of immunohistochemical double staining by computer-assisted image processing. Five human ductal breast cancers were examined. The simultaneous demonstration of both receptors consists in the following principal steps:

关键词： Breast cancer Estrogen- and progesterone receptor Immunohistochemical double staining technique computer-assisted image processing

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Resection of Midfacial Tumors assisted by Virtual Planning Based on CT/MRI Multimodal image Fusion: A Cohort Study

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JOURNAL OF MAXILLOFACIAL & ORAL SURGERY 2025年 1-11页

作者： Hu, Lei-Hao Zhou, Wei Zhang, Wen-Bo Yu, Yao Sun, Zhi-Peng Peng, Xin Peking Univ Sch & Hosp Stomatol Dept Gen Dent Beijing 100081 Peoples R China Natl Ctr Stomatol Beijing 100081 Peoples R China Natl Clin Res Ctr Oral Dis Beijing 100081 Peoples R China Natl Engn Res Ctr Oral Biomat & Digital Med Device Beijing 100081 Peoples R China Beijing Key Lab Digital Stomatol Beijing 100081 Peoples R China NHC Key Lab Digital Stomatol Beijing 100081 Peoples R China NMPA Key Lab Dent Mat Beijing 100081 Peoples R China Peking Univ Sch & Hosp Stomatol Dept Oral & Maxillofacial Surg 22 Zhongguancun South Ave Beijing 100081 Peoples R China Peking Univ Sch & Hosp Stomatol Dept Oral & Maxillofacial Radiol Beijing 100081 Peoples R China

ObjectiveThis cohort study aimed to preliminarily explore the effect of midfacial tumor resection procedure with assistance of virtual planning based on CT/MRI multimodal image *** the experimental cohort, patients with midfacial tumors receiving treatment from February 2019 to March 2021 were enrolled. Virtual planning was completed preoperatively based on CT/MRI multimodal image fusion. Tumor resection was performed under virtual planning-based surgical navigation, and intraoperative frozen sections were taken to determine resection margin status. For the control cohort, patients with midfacial tumors treated by the same surgical team were enrolled according to the same criteria. Patients underwent surgery assisted by virtual planning based on single-modality CT images. Resection margin and survival status were compared between groups during *** twenty-nine patients were enrolled. The resection margin status was significantly different between groups at the per-margin level (experimental group: 100% [66/66] negative;control group: 90.9% negative [70/77];P = 0.014). During follow-up period, two patients in experimental cohort and seven patients in control cohort confirmed local *** with midfacial tumor who underwent virtual planning based on CT/MRI multimodal image fusion were more likely to have tumor-free resection margins. The use of the image fusion procedure may improve treatment outcomes in such patients.

关键词： Multimodal imaging Resection margin Surgical navigation systems computer-assisted image processing Midfacial tumor

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Performance of image processing analysis and a deep convolutional neural network for the classification of oral cancer in fluorescence visualization

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International Journal of Oral and Maxillofacial Surgery 2025年第6期54卷 511-518页

作者： Morikawa, T. Shingyouchi, M. Ariizumi, T. Watanabe, A. Shibahara, T. Katakura, A. Department of Oral and Maxillofacial Surgery Tokyo Dental College Tokyo Japan Oral and Maxillofacial Surgery Mitsuwadai General Hospital Chiba Japan Department of Oral Pathobiological Science and Surgery Tokyo Dental College Tokyo Japan

The aim of this prospective study was to determine the effectiveness of screening using image processing analysis and a deep convolutional neural network (DCNN) to classify oral cancers using non-invasive fluorescence visualization. The study included 1076 patients with diseases of the oral mucosa (oral cancer, oral potentially malignant disorders (OPMDs), benign disease) or normal mucosa. For oral cancer, the rate of fluorescence visualization loss (FVL) was 96.9%. Regarding image processing, multivariate analysis identified FVL, the coefficient of variation of the G value (CV), and the G value ratio (VRatio) as factors significantly associated with oral cancer detection. The sensitivity and specificity for detecting oral cancer were 96.9% and 77.3% for FVL, 80.8% and 86.4% for CV, and 84.9% and 87.8% for VRatio, respectively. Regarding the performance of the DCNN for image classification, recall was 0.980 for oral cancer, 0.760 for OPMDs, 0.960 for benign disease, and 0.739 for normal mucosa. Precision was 0.803, 0.821, 0.842, and 0.941, respectively. The F-score was 0.883, 0.789, 0.897, and 0.828, respectively. Sensitivity and specificity for detecting oral cancer were 98.0% and 92.7%, respectively. The accuracy for all lesions was 0.851, average recall was 0.860, average precision was 0.852, and average F-score was 0.849. © 2024 International Association of Oral and Maxillofacial Surgeons

关键词： Artificial intelligence computer-assisted image processing Early detection of cancer Fluorescence Head and neck neoplasms

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Convolutional neural networks for automatic MR classification of myocardial iron overload in thalassemia major patients

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EUROPEAN RADIOLOGY 2025年第3期35卷 1522-1532页

作者： Positano, Vincenzo Meloni, Antonella De Santi, Lisa Anita Pistoia, Laura Borsellino, Zelia Cossu, Alberto Massei, Francesco Sanna, Paola Maria Grazia Santarelli, Maria Filomena Cademartiri, Filippo Fdn G Monasterio CNR Reg Toscana Bioengn Unit Pisa Italy Fdn G Monasterio CNR Reg Toscana Dept Radiol Pisa Italy Univ Pisa Dept Informat Engn Pisa Italy Fdn G Monasterio CNR Reg Toscana Unita Operat Complessa Ric Clin Pisa Italy ARNAS Civ Benfratelli Di Cristina Unita Operat Complessa Ematol Talassemia Palermo Italy Azienda Osped Univ S Anna Unita Operat Radiol Univ Ferrara Italy Azienda Osped Univ Pisana UO Oncoematol Pediat Pisa Italy Azienda Osped Univ Sassari Serv Trasfus Aziendale Sassari Italy CNR Inst Clin Physiol Pisa Italy

Objectives To develop a deep-learning model for supervised classification of myocardial iron overload (MIO) from magnitude T2* multi-echo MR images. Materials and methods Eight hundred twenty-three cardiac magnitude T2* multi-slice, multi-echo MR images from 496 thalassemia major patients (285 females, 57%), labeled for MIO level (normal: T2* > 20 ms, moderate: 10 <= T2* <= 20 ms, severe: T2* < 10 ms), were retrospectively studied. Two 2D convolutional neural networks (CNN) developed for multi-slice (MS-HippoNet) and single-slice (SS-HippoNet) analysis were trained using 5-fold cross-validation. Performance was assessed using micro-average, multi-class accuracy, and single-class accuracy, sensitivity, and specificity. CNN performance was compared with inter-observer agreement between radiologists on 20% of the patients. The agreement between patients' classifications was assessed by the inter-agreement Kappa test. Results Among the 165 images in the test set, a multi-class accuracy of 0.885 and 0.836 was obtained for MS- and SS-Hippo-Net, respectively. Network performances were confirmed on external test set analysis (0.827 and 0.793 multi-class accuracy, 29 patients from the CHMMOTv1 database). The agreement between automatic and ground truth classification was good (MS: kappa = 0.771;SS: kappa = 0.614), comparable with the inter-observer agreement (MS: kappa = 0.872, SS: kappa = 0.907) evaluated on the test set. Conclusion The developed networks performed classification of MIO level from multiecho, bright-blood, and T2* images with good performances. Key Points Question MRI T2* represents the established clinical tool for MIO assessment. Quality control of the image analysis is a problem in small centers. Findings Deep learning models can perform MIO staging with good accuracy, comparable to inter-observer variability of the standard procedure. Clinical relevance CNN can perform automated staging of cardiac iron overload from multiecho MR sequences facilitating no

关键词： Deep learning computer-assisted image processing Iron overload Hemochromatosis Magnetic resonance imaging

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Different corpus callosum in panic disorder

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PSYCHIATRY RESEARCH-NEUROIMAGING 2025年 349卷 111990-111990页

作者： Kasikci, Halim Omer Baykara, Sema Balci, Hale Nur Baykara, Murat Erenkoy Psychiat & Neurol Training & Res Hosp Dept Family Med Istanbul Turkiye Erenkoy Psychiat & Neurol Training & Res Hosp Dept Psychiat Istanbul Turkiye Firat Univ Fac Med Dept Psychiat Elazig Turkiye Haydarpasa Numune Training & Res Hosp Dept Radiol Istanbul Turkiye

Background: Despite hypotheses regarding the neurobiology of panic disorder (PD), its neurobiological basis is still unknown. Study results support that the individual differences in corpus callosum (CC) properties could reflect trait based alterations that predispose individuals to higher anxiety sensitivity, and to disorders associated with stress such as PD. Neuroimaging studies with panic disorder have not been sufficient to explain the pathophysiology of the disease. The aim of this study is to provide additional information for studies examining the etiology of PD by comparing the corpus callosum, a region associated with attention, anxiety, and somatic complaints, on sagittal MRI images of PD patients with the corpus callosum of healthy individuals. Methods: T2-weighted MRI images of 164 patients diagnosed with PD and 78 controls selected from Hospital Information System (HIS) and meeting the study criteria were evaluated by shape analysis method. Results: There were differences between the shapes and areas of the CC in the mid-sagittal images of the PD patients and healthy controls. Conclusions: This study findings highlighted the variable dimensional and subregional properties of CC in PD patients. This study could shed light on future studies about PD etiology, diagnosis and treatment.

关键词： computer-assisted image processing Corpus callosum Magnetic resonance imaging Panic disorder

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Validation of a novel tool for automated tooth modelling by fusion of CBCT-derived roots with the respective IOS-derived crowns

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JOURNAL OF DENTISTRY 2025年 153卷 105546-105546页

作者： Baldini, Benedetta Papasratorn, Dhanaporn Fagundes, Fernanda Bulhoes Fontenele, Rocharles Cavalcante Jacobs, Reinhilde Katholieke Univ Leuven Fac Med Dept Imaging & Pathol OMFS IMPATH Res Grp Leuven Belgium Univ Hosp Leuven Dept Oral & Maxillofacial Surg Leuven Belgium Politecn Milan Dept Elect Informat & Bioengn Milan Italy Osped Maggiore Policlin UOC Maxillo Facial Surg & Dent Fdn IRCCS Ca Granda Milan Italy Mahidol Univ Fac Dent Dept Oral & Maxillofacial Radiol Bangkok Thailand Univ Estadual Campinas Piracicaba Dent Sch Dept Oral Diag Div Oral Radiol Piracicaba SP Brazil Karolinska Inst Dept Dent Med Stockholm Sweden

Objectives: To validate a novel artificial intelligence (AI)-based tool for automated tooth modelling by fusing cone beam computed tomography (CBCT)-derived roots with corresponding intraoral scanner (IOS)-derived crowns. Methods: A retrospective dataset of 30 patients, comprising 30 CBCT scans and 55 IOS dental arches, was used to evaluate the fusion model at full arch and single tooth levels. AI-fused models were compared with CBCT tooth segmentation using point-to-point surface distances-reported as median surface distance (MSD), root mean square distance (RMSD), and Hausdorff distance (HD)-alongside visual assessments. Qualitative assessment included visual inspection of CBCT multiplanar views. The automated fused model was also compared to expert- manual fusions for single tooth analysis in terms of accuracy, time efficiency, and consistency. Results: AI-based fusion evaluation showed mean values of MSD, RMSD, and HD of 4 mu m, 114 mu m, and 940 mu m for full arch;5 mu m, 104 mu m, and 503 mu m for single tooth analysis. Qualitative assessment showed discrepancies between fused tooth outline and CBCT tooth margin lower than 1 voxel for 59% of cases. AI-based fusion showed high similarity with expert-manual fusions with median MSD, RMSD, and HD values of 28 mu m, 104 mu m, and 576 mu m, respectively. However, AI-based fusion was 32 times faster than manual fusion. Considering the time required for manual fusion, intra-observer agreement was high (ICC 0.93), while inter-observer agreement was moderate (ICC 0.48). Conclusion: The AI-based CBCT/IOS fusion demonstrated clinically acceptable accuracy, efficiency, and consistency, offering substantial time savings and robust performance across different patients and imaging devices. Clinical significance: Manual CBCT/IOS fusion performed by experts is effective but labor-intensive and timeconsuming. AI algorithms show a remarkable ability to minimize human variability, resulting in more reliable and efficient fusion.

关键词： multimodal image fusion digital dentistry artificial intelligence cone-beam computed tomography intra-oral scanner 3D imaging computer-assisted image processing

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Foundation models in pathology

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PATHOLOGIE 2025年第3期46卷 152-155页

作者： Klauschen, Frederick Dippel, Jonas Mueller, Klaus-Robert Ludwig Maximilians Univ Munchen Pathol Inst Thalkirchner Str 36 D-80337 Munich Germany Berlin Inst Fdn Learning & Data BIFOLD Berlin Germany Bayer Zentrum Krebsforschung BZKF Munich Germany Tech Univ Berlin Machine Learning Grp Berlin Germany Aignostics GmbH Berlin Germany Korea Univ Dept Artificial Intelligence Seoul South Korea Max Planck Inst Informat Saarbrucken Germany

Foundation models prepare neural networks for applications in specific domains, such as speech applications or image analysis, through self-supervised pretraining. These models can be adapted for specific applications, such as histopathological diagnostics. While adaptation still requires supervised training, AI applications based on foundation models achieve significantly better prediction accuracy with fewer training data compared to conventional approaches. This article introduces the topic and provides an overview of foundation models in pathology.

关键词： Artificial intelligence computer-assisted image processing Histopathology Machine learning Machine learning Foundation models Foundation models Neural networks Neural networks Machine learning Foundation models Neural networks

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CT metal artefact reduction for hip and shoulder implants using novel algorithms and machine learning: A systematic review with pairwise and network meta-analyses

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RADIOGRAPHY 2025年第1期31卷 36-52页

作者： Amadita, K. Gray, F. Gee, E. Ekpo, E. Jimenez, Y. Univ Sydney Fac Med & Hlth Discipline Med Imaging Sci Camperdown NSW 2006 Australia

Introduction: Many tools have been developed to reduce metal artefacts in computed tomography (CT) images resulting from metallic prosthesis;however, their relative effectiveness in preserving image quality is poorly understood. This paper reviews the literature on novel metal artefact reduction (MAR) methods targeting large metal artefacts in fan-beam CT to examine their effectiveness in reducing metal artefacts and effect on image quality. Methods: The PRISMA checklist was used to search for articles in five electronic databases (MEDLINE, Scopus, Web of Science, IEEE, EMBASE). Studies that assessed the effectiveness of recently developed MAR method on fan-beam CT images of hip and shoulder implants were reviewed. Study quality was assessed using the National Institute of Health (NIH) tool. Meta-analyses were conducted in R, and results that could not be meta-analysed were synthesised narratively. Results: Thirty-six studies were reviewed. Of these, 20 studies proposed statistical algorithms and 16 used machine learning (ML), and there were 19 novel comparators. Network meta-analysis of 19 studies showed that Recurrent Neural Network MAR (RNN-MAR) is more effective in reducing noise (LogOR 20.7;95 % CI 12.6-28.9) without compromising image quality (LogOR 4.4;95 % CI-13.8-22.5). The network meta-analysis and narrative synthesis showed novel MAR methods reduce noise more effectively than baseline algorithms, with five out of 23 ML methods significantly more effective than Filtered Back Projection (FBP) (p < 0.05). Computation time varied, but ML methods were faster than statistical algorithms. Conclusion: ML tools are more effective in reducing metal artefacts without compromising image quality and are computationally faster than statistical algorithms. Overall, novel MAR methods were also more effective in reducing noise than the baseline reconstructions. Implications for practice: Implementation research is needed to establish the clinical suitability of ML MAR in

关键词： Artefacts X-ray computed tomography Machine learning Metal implant computer-assisted image processing Correction algorithm

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