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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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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 Institute of Pathology Department of General and Special Pathology University of the Saarland 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: The primary antisera against the ER (monoclonal rat antibody) and the PR (monoclonal mouse antibody) are incubated simultaneously, but only the anti-ER antibody is demonstrated in the first staining step by using a goat anti-rat antibody as the linking antibody and the PAP complex from the rat, both antisera from the ER-ICA kit. The result is stored as a digitized grey image (“1. object image”). Then the colored end product and the residual peroxidase activity of the PAP complex are removed. In the second staining sequence the anti-PR-antibody is demonstrated by using a rabbit anti-mouse antibody as the linking antibody and the PAP complex from the mouse. The result is exactly positioned and also stored as a digitized grey image (“2. object image”). Though antibodies raised in different species were used, cross-reactivity could not be avoided. Grey values generated by cross-reactivity between the different anti-body systems are evaluated in negative controls and are eliminated in the object images. The remaining (specifically stained) structures of both object images are copied into a final image so that the topographical relationship of the ER and the PR becomes obvious. The results show that in the five carcinomas investigated three types of receptor-positive tumour cells can be distinguished: Cells which coexpress the ER and the PR (1), cells which express either the ER (2) or the PR (3). The number of tumour cells showing one of these expression patterns varies from tumour to tumour.

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

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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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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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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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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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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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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 Skin-Lesion image-Matching Algorithm Based on computer Vision Technology

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TELEMEDICINE AND E-HEALTH 2016年第1期22卷 45-50页

作者： Chen, Raymond H. Snorrason, Magnus Enger, Shelley M. Mostafa, Eslam Ko, Justin M. Aoki, Valeria Bowling, Jonathan Lubax Inc Los Angeles CA 90013 USA Stanford Med Sch Dept Dermatol Redwood City CA USA Univ Sao Paulo Sch Med Dept Dermatol Sao Paulo Brazil Manor Hosp Oxford England

Background:Melanoma incidence is increasing globally, but consistently accurate skin-lesion classification methods remain elusive. We developed a simple software system to classify potentially all types of skin lesions. In the current study, we evaluated the system's ability to identify melanomas with a diameter of 10mm or *** and Methods:The skin-lesion classification system is composed of a proprietary database of nearly 12,000 diagnosed skin-lesion images and a computer algorithm based on the principles of content-based image retrieval. The algorithm compares characteristics of new skin-lesion images with images in the database to identify the nearest-match ***:Nearly all classification accuracy measures for this new system exceeded 90%, with results for sensitivity of 90.4% (95% confidence interval, 85.6-93.7%), specificity of 91.5% (85.4-95.2%), positive predictive value of 94.5% (90.4-96.9%), negative predictive value of 85.5% (78.7-90.4%), and overall classification accuracy of 90.8% (87.2-93.4%).Conclusions:The image-matching algorithm performed with high accuracy for the classification of larger melanomas. Furthermore, the system does not require a dermoscope or any other specialized hardware;any close-focusing camera will do. This system has the potential to be an inexpensive and accurate tool for the evaluation of skin lesions in ethnically and geographically diverse populations.

关键词： melanoma skin neoplasms store and forward teledermatology databases computer-assisted image processing

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Iterative scatter correction for grid-less skeletal radiography allows improved image quality equal to an antiscatter grid in adjunct with dose reduction: a visual grading study of 20 body donors

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ACTA RADIOLOGICA 2019年第6期60卷 735-741页

作者： Lisson, Christoph G. Lisson, Catharina S. Kleiner, Sebastian Regier, Marc Beer, Meinrad Schmidt, Stefan A. Univ Hosp Ulm Dept Diagnost & Intervent Radiol Albert Einstein Allee 23 D-89081 Ulm Germany Univ Med Ctr Hamburg Eppendorf Dept Diagnost & Intervent Radiol Hamburg Germany

Background Iterative scatter correction (ISC) is a new technique applicable to plain radiography;comparable to iterative reconstruction for computed tomography, it promises dose reduction and image quality improvement. ISC for bedside chest X-rays has been applied and evaluated for some time and has recently been commercially offered for plain skeletal radiography. Purpose To analyze the potential of ISC for plain skeletal radiography with regard to image quality improvement, dose reduction, and replacement for an antiscatter grid. Material and Methods A total of 385 radiographs with different imaging protocols of the pelvis and cervical spine were acquired from 20 body donors. Radiographs were rated by four radiologists. Ratings were analyzed with visual grading characteristics (VGC) analysis. The area under the VGC curve was used as a measure of difference in image quality. Results Without ISC, the grid-less images were rated significantly worse than their grid-based counterparts (0.389, P = 0.005);adding ISC made image quality equal (0.498;P = 0.963). In grid-less imaging, reduction of dose by 50% led to significant image quality impairment (0.415, P = 0.001);this was fully counterbalanced when ISC was added (0.512;P = 0.588). Conclusion ISC for plain skeletal radiography has the ability to replace the antiscatter grid without image quality impairment, to improve image quality in grid-less imaging, and to reduce patient radiation dose by 50% without substantial loss in image quality.

关键词： Skeletal - appendicular skeletal - axial radiation safety digital radiography computer-assisted image processing technology assessments

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