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Journal articles on the topic 'Medical image anonymization'
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Ueda, Satoshi. "Activities for Anonymization in Image Medical Systems." Japanese Journal of Radiological Technology 75, no. 9 (2019): 1109–11. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.9.1109.
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Gießler, Fina, Maximilian Thormann, Bernhard Preim, Daniel Behme, and Sylvia Saalfeld. "Facial Feature Removal for Anonymization of Neurological Image Data." Current Directions in Biomedical Engineering 7, no. 1 (August 1, 2021): 130–34. http://dx.doi.org/10.1515/cdbme-2021-1028.
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Abstract Interdisciplinary exchange of medical datasets between clinicians and engineers is essential for clinical research. Due to the Data Protection Act, which preserves the rights of patients, full anonymization is necessary before any exchange can take place. Due to the continuous improvement of image quality of tomographic datasets, anonymization of patient-specific information is not sufficient. In this work, we present a prototype that allows to reliably obscure the facial features of patient data, thus enabling anonymization of neurological datasets in image space.
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Sun, Hanxi, Jason Plawinski, Sajanth Subramaniam, Amir Jamaludin, Timor Kadir, Aimee Readie, Gregory Ligozio, David Ohlssen, Mark Baillie, and Thibaud Coroller. "A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)." PLOS ONE 18, no. 7 (July 6, 2023): e0280316. http://dx.doi.org/10.1371/journal.pone.0280316.
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Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy.
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Shibata, Hisaichi, Shouhei Hanaoka, Takahiro Nakao, Tomohiro Kikuchi, Yuta Nakamura, Yukihiro Nomura, Takeharu Yoshikawa, and Osamu Abe. "Practical Medical Image Generation with Provable Privacy Protection Based on Denoising Diffusion Probabilistic Models for High-Resolution Volumetric Images." Applied Sciences 14, no. 8 (April 20, 2024): 3489. http://dx.doi.org/10.3390/app14083489.
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Local differential privacy algorithms combined with deep generative models can enhance secure medical image sharing among researchers in the public domain without central administrators; however, these images were limited to the generation of low-resolution images, which are very insufficient for diagnosis by medical doctors. To enhance the performance of deep generative models so that they can generate high-resolution medical images, we propose a large-scale diffusion model that can, for the first time, unconditionally generate high-resolution (256×256×256) volumetric medical images (head magnetic resonance images). This diffusion model has 19 billion parameters, but to make it easy to train it, we temporally divided the model into 200 submodels, each of which has 95 million parameters. Moreover, on the basis of this new diffusion model, we propose another formulation of image anonymization with which the processed images can satisfy provable Gaussian local differential privacy and with which we can generate images semantically different from the original image but belonging to the same class. We believe that the formulation of this new diffusion model and the implementation of local differential privacy algorithms combined with the diffusion models can contribute to the secure sharing of practical images upstream of data processing.
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Yordanova, Mariana. "Recent Applications of Cloud Computing in Medical Imaging: Advances in Medical Image Analysis and Storage of Medical Imaging Data." Advances in Social Sciences Research Journal 11, no. 5 (May 31, 2024): 267–71. http://dx.doi.org/10.14738/assrj.115.16941.
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Purpose: The purpose of this article is to offer an overview of the recent applications of cloud computing in medical imaging and their advances in the field. It reviews existing scientific and academic literature on cloud computing-based platforms and solutions for medical image analysis and storage of medical imaging data. Materials and methods: This article uses available scientific literature on the applications of cloud computing in medical imaging from PubMed, Google Scholar and ScienceDirect. Results: The review shows that interest and research in cloud computing applications in medical imaging has increased in recent years. This has led to new and more effective platforms and solutions for medical image analysis and storage of medical imaging data. Innovative applications of cloud computing in medical imaging try to address ethical and security concerns using authentication, encryption, anonymization and access controls. Conclusions: Cloud computing offers promising applications in medical imaging. Notwithstanding, further research is necessary to demonstrate their effectiveness, safety and security in a medical setting.
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Jeong, Yeon Uk, Soyoung Yoo, Young-Hak Kim, and Woo Hyun Shim. "De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology." Journal of Medical Internet Research 22, no. 12 (December 10, 2020): e22739. http://dx.doi.org/10.2196/22739.
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Background High-resolution medical images that include facial regions can be used to recognize the subject’s face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data. According to the Health Insurance Portability and Accountability Act (HIPAA) privacy rules, full-face photographic images and any comparable image are direct identifiers and considered as protected health information. Moreover, the General Data Protection Regulation (GDPR) categorizes facial images as biometric data and stipulates that special restrictions should be placed on the processing of biometric data. Objective This study aimed to develop software that can remove the header information from Digital Imaging and Communications in Medicine (DICOM) format files and facial features (eyes, nose, and ears) at the 2D sliced-image level to anonymize personal information in medical images. Methods A total of 240 cranial magnetic resonance (MR) images were used to train the deep learning model (144, 48, and 48 for the training, validation, and test sets, respectively, from the Alzheimer's Disease Neuroimaging Initiative [ADNI] database). To overcome the small sample size problem, we used a data augmentation technique to create 576 images per epoch. We used attention-gated U-net for the basic structure of our deep learning model. To validate the performance of the software, we adapted an external test set comprising 100 cranial MR images from the Open Access Series of Imaging Studies (OASIS) database. Results The facial features (eyes, nose, and ears) were successfully detected and anonymized in both test sets (48 from ADNI and 100 from OASIS). Each result was manually validated in both the 2D image plane and the 3D-rendered images. Furthermore, the ADNI test set was verified using Microsoft Azure's face recognition artificial intelligence service. By adding a user interface, we developed and distributed (via GitHub) software named “Deface program” for medical images as an open-source project. Conclusions We developed deep learning–based software for the anonymization of MR images that distorts the eyes, nose, and ears to prevent facial identification of the subject in reconstructed 3D images. It could be used to share medical big data for secondary research while making both data providers and recipients compliant with the relevant privacy regulations.
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David Odera. "A survey on techniques, methods and security approaches in big data healthcare." Global Journal of Engineering and Technology Advances 14, no. 2 (February 28, 2023): 093–106. http://dx.doi.org/10.30574/gjeta.2023.14.2.0035.
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A huge percentage of people especially in developed countries spend a good chunk of their wealth in managing their health conditions. In order to adequately administer healthcare, governments and various organizations have embraced advanced technology for automating the health industry. In recent past, electronic health records have largely been managed by Enterprise Resource Planning and legacy systems. Big data framework steadily emerge as the underlying technology in healthcare, which offers solutions that limits capacity of others systems in terms of storage and reporting. Automation through cloud services supported by storage of structured and unstructured health data in heterogeneous environment has improved service delivery, efficiency, medication, diagnosis, reporting and storage in healthcare. The argument support the idea that big data healthcare still face information security concern, for instance patient image sharing, authentication of patient, botnet, correlation attacks, man-in-the-middle, Distributed Denial of Service (DDoS), blockchain payment gateway, time complexities of algorithms, despite numerous studies conducted by scholars in security management for big data in smart healthcare. Some security technique include digital image encryption, steganography, biometrics, rule-based policy, prescriptive analysis, blockchain contact tracing, cloud security, MapReduce, machine-learning algorithms, anonymizations among others. However, most of these security solutions and analysis performed on structured and semi-structured data as opposed to unstructured data. This may affect the output of medical reporting of patients’ condition particularly on wearable devices and other examinations such as computerized tomography (CT) Scans among others. A major concern is how to identify inherent security vulnerabilities in big healthcare, which generate images for transmission and storage. Therefore, this paper conducted a comparative survey of solutions that specifically safeguards structured and unstructured data using systems that run on big data frameworks. The literature highlights several security advancements in cryptography, machine learning, anonymization and protocols. Most of these security frameworks lacks implementation evidence. A number of studies did not provide comprehensive performance metrics (accuracy, error, recall, precision) of the models besides using a single algorithm without validated justification. Therefore, a critique on the contribution, performance and areas of improvements discussed and summarized in the paper.
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Chin, Trisha J. M., Gillian X. M. Chin, James Sutherland, Andrew Coon, Colin Morton, and Colin Fleming. "BT24 Pseudonymization for artificial intelligence skin lesion datasets: a real-world feasibility study." British Journal of Dermatology 191, Supplement_1 (June 28, 2024): i199—i200. http://dx.doi.org/10.1093/bjd/ljae090.421.
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Abstract The use of patient data for artificial intelligence (AI) research should be transparent, rigorous and accountable. In the UK, the General Data Protection Regulation, Data Protection Act 2018 and General Medical Council govern data handling and patients’ rights to privacy. We report on our multistep pseudonymization protocol for real-world skin lesion datasets, in preparation for research within a trusted research environment (TRE). Firstly, patients referred from primary care are triaged for community locality and imaging centre (CLIC) suitability. There, trained healthcare professionals capture lesion images (dermoscopic, macroscopic and regional) and patient information using a mobile application on trust-certified devices. Training is standardized across all CLIC sites, with specific anonymization training on removing in-frame clothing and jewellery, device positioning, and magnification to minimize identifiable features like eyes, nose and ears. Lesion datasets (paired images and clinical information) are subsequently transferred to an image management system (IMS) hosted on our trust-secured network. Within the IMS, images are manually inspected, and those with identifiable tattoos and piercings are excluded. All regional images are also excluded from transfer to the TRE. Before transfer to the TRE, images undergo a further round of review. Data fields are manually checked for identifiable patient information, patient names are removed, and dates of birth are rounded to 3-month granularity. The job ID, patient’s hospital number, date of clinical episode and responsible photographer are replaced with randomly generated project-specific identifiers. In an initial study period, 658 of 963 (68%) captured lesion datasets have undergone IMS manual inspection. Of these, 24 lesion datasets were excluded for identifiable features, 10 (41%) for more than one-third of the face being visible, 9 (38%) for full iris visibility, and 5 (21%) for tattoos. On breakdown by anatomical location these images were of the face (19, 80%), torso (2, 8%), limbs (2, 8%) and neck (1, 4%). The remaining 634 datasets (96%) were securely transferred to the TRE, where a further 5% were excluded due to potential identifiability. Although full anonymization is desirable, it is usually achieved by aggregating patient data. Pseudonymization, which allows for future reidentification in a secured fashion, strikes the balance between patient data privacy and clinical governance, while retaining a level of granularity sufficient for meaningful analysis. Currently, this protocol is manually intensive with room to partly automate. Use of common standardized protocols will strengthen the public trust in clinical AI.
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Sulema, Ye S., and A. I. Dychka. "Software system of automatic identification and distributed storage of patient medical data." System technologies 3, no. 146 (May 11, 2023): 134–48. http://dx.doi.org/10.34185/1562-9945-3-146-2023-13.
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Due to the rapid development of information technologies, informatization in the medical industry is essential. The main component of electronic health care is medical information systems designed for the accumulation, processing, analysis and transmis-sion of medical data. In the medical field, specialized software products are used to per-form diagnostic studies, process the results of laboratory tests, and make decisions at the stage of establishing a diagnosis. The use of mobile devices in medical information systems is developing. However, the degree of automation of processes in the provision of medical services and the protection of the personal and medical data of patients is still insufficient. The purpose of the research is to create a basic architecture of a software system that would simplify the process of developing software for automated input, processing, search and confidential patient access to their medical data in a medical information system based on multi-color barcoding of information using mobile devices. The architecture of the software system is proposed, in which, based on the princi-ples of distribution, anonymization, and data ownership, a patient can provide access to medical personnel to their medical data by reading a multi-color interference-resistant barcode from one smartphone (patient’s) by the camera of another smartphone (doctor’s). It is shown that in order to ensure the reliability of such transmission, it is neces-sary to use an interference-resistant barcode, which would ensure the integrity of the data in the conditions of possible distortion of the barcode image (change in lighting, scanning angle, trembling of the operator's hand, blurring or skewing of the image, etc.). The use of mobile devices for the barcode method of transmission and processing of data allows providing the protected electronic co-operating of a patient and a doctor both directly and remotely. It guarantees high reliability and confidentiality of the ex-change of data. The proposed technical solutions make it possible to improve the quality of medi-cal care and strengthen the protection of the patient's medical data.
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Cordeiro, Natália, Gil Facina, Afonso Nazário, Vanessa Sanvido, Joaquim Araujo Neto, Morgana Silva, and Simone Elias. "Abstract PO2-29-02: Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for Artificial Intelligence and Teaching." Cancer Research 84, no. 9_Supplement (May 2, 2024): PO2–29–02—PO2–29–02. http://dx.doi.org/10.1158/1538-7445.sabcs23-po2-29-02.
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Abstract Objectives: According to the World Health Organization (WHO), breast cancer is the most frequent malignant neoplasia and leading cause of cancer death among women worldwide. Low- and middle-income countries hold the worst survival rates mainly owing to a lack of access to appropriate diagnosis and treatment related resources. For proper early diagnosis, it is established that besides the physical structure itself (e.g., mammography units), there's a need for adequate interpretation of imaging and that might be a particularly major problem in low-income societies once there is a tendency of greater education setbacks. Mammography datasets can improve this resource-driven gap by enabling the development of artificial intelligence technologies (AI) which can make breast cancer diagnosis more accurate in a cost-effective and scalable way. We aim to create a new database of high quality digital mammography images suitable for AI development and education. Methods: Our mammography database was developed by means of retrospective selection of 100 exams performed by Hospital São Paulo - Federal University of São Paulo ranging from 2019 to 2023. The project is assumed to be safe, versatile, and usable, and required an extensive search for the appropriate tool. Ambra Health, an American company, has developed cloud-based software for medical image management and stood out as a viable alternative. Their platform meets international data security criteria, they also made the intended careful customization possible, in addition to the possibility of associating image and text attachments. The categories were created in accordance with the BI-RADS® descriptors, a wide range of clinical scenarios and additional materials available, and they served as the basis for the advanced search feature, which intuitively filters exams that meet the selected criteria simultaneously. The platform was integrated with an automatic anonymization system upon upload, ensuring data privacy. After submission, the exams are retained in a restricted area for anonymization verification, categorization, and attachment management, before being released to the end-user. So as to broaden geographic coverage, the descriptors were entered in American English, respecting the origin of the BI-RADS® lexicon, as for the website structure, automatic translation to the accessing browser standard language was selected. Results: Our website is active and available at http://mamografia.unifesp.br, with access granted upon a simple registration process. 941 mammography images from 100 anonymized cases, 62% of which include 3D images, can be filtered based on the combination of 113 clinical and imaging variables, as well as attachment availability. The language is adaptable to the user's native language, and categorized searches can be accessed directly from the browser or downloaded as customized datasets. Additionally, features such as saved searches or starred exams are also available. Conclusion: We have developed an online and free mammography database that is completely innovative by integrating various resources into a single platform. We provide high-resolution and 3D digital images that can be searched using an advanced search system. Moreover, we offer supplementary clinical information in various attachment formats, favoring a rich clinical correlation. In this way, we have achieved the ambivalence of our goal, which was to promote education and research. *"images speak louder than words" Database: https://mamografia.unifesp.br Tutorials: https://www.youtube.com/@Mamografiaunifesp e-mail: acesso@mamografia.unifesp.br password: acesso@mamografia12 (valid until dec/23) Citation Format: Natália Cordeiro, Gil Facina, Afonso Nazário, Vanessa Sanvido, Joaquim Araujo Neto, Morgana Silva, Simone Elias. Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for Artificial Intelligence and Teaching [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-29-02.
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Cerrato, Tania Raquel. "Use of artificial intelligence to improve access to initial leukemia diagnosis in low- and middle-income countries." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14117-e14117. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14117.
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e14117 Background: Disparities in access to cancer diagnostic methods affect especially low and middle-income countries (LMIC). Honduras, a country with limited resources of laboratory, and trained personnel to diagnose leukemia have an estimated incidence of leukemia of 5.8 per 100,000 person-years and the mortality is 4.8 per 100,000 person-years. This country has a single immunophenotyping center and similar to many other LMICs, the preliminary diagnosis of leukemia is performed by a morphological study of blood. Machine learning (ML) is a form of artificial intelligence (AI) that has the potential to improve medical attention in several medical fields. The purpose of this study is to evaluate the impact of ML use, as a method to reduce the time interval and the access to a diagnosis of leukemia in Honduras, where the current average time to perform an initial diagnosis of leukemia is from 2 weeks to 3 months. Methods: A quantitative correlational study was designed. With the help of local informatic developers, an automated image processing algorithm was designed, which was fed with a total of 1009, digital images of bone marrow aspirates and peripheral blood of patients with proven leukemia through immunophenotyping. The images were captured using a microscope Carl Zeiss lens with a 100x objective, the process included the segmentation of leukemia cells for its analysis. Bivariate outcomes were assessed using the Pearson chi-squared test. Results: From 2016 to 2018, a total of 341 samples of patients with any symptom of leukemia were included. After anonymization of patient identification, the samples were analyzed using our algorithm by comparing the cells with its database. Posteriorly, an expert hematologist performed an analysis of the sample. A total of 20 samples (5.8%), were diagnosed with a preliminary diagnosis of leukemia. Of the 20 samples, a total of 10(50%) were acute myeloid leukemia, 6 samples (30%) lymphoblastic leukemia and the remaining 4 samples (20%) were compatible with chronic myeloid leukemia. The average time to make an initial diagnosis of leukemia was 75% and 24% in 24 and 48 hours respectively. Local hematologists managed to make treatment decisions earlier with benefit to the patients. In 19 of the samples (95%), there was correspondence between the morphology diagnosis obtained by our algorithm and the immunophenotyping diagnosis. Conclusions: This preliminary study demonstrates that the use of artificial intelligence constitutes an important element to improve access and shortening of the time required to obtain a diagnosis of leukemia in LMCIs, and represents a method to reduce disparities in access to diagnosis of hematological cancers.
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Solar, Mauricio, Victor Castañeda, Ricardo Ñanculef, Lioubov Dombrovskaia, and Mauricio Araya. "A Data Ingestion Procedure towards a Medical Images Repository." Sensors 24, no. 15 (August 1, 2024): 4985. http://dx.doi.org/10.3390/s24154985.
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This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.
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Shcheglov, B. O., N. I. Bezulenko, S. A. Atashchikov, and S. N. Scheglova. "Virtual Atlas of Personified Human Anatomy “SkiaAtlas” and the Possibility of Its Application." Vestnik NSU. Series: Information Technologies 18, no. 1 (2020): 83–93. http://dx.doi.org/10.25205/1818-7900-2020-18-1-83-93.
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The work is devoted to the description of the structure of the developed SkiaAtlas software, which is focused on working with individual anatomical models of the human body and physiological parameters of the patient. The problem of using mock-up and post-sectional material in teaching medical students, and why the developed information system has advantages over these models, is shown. Virtual anatomical models were obtained from anonymous DICOM images of magnetic resonance imaging (MRI) and computed tomography (CT). The subsystems of the information system are described: a PACS server where all data is stored (server part) and a web application where the user works with data (client part). The information system modules implemented in the form of various software products are described in detail: data import module, anonymization module, DBMS module, visualization module, etc. The operation of these modules is illustrated schematically. It is shown in what programming languages and frameworks this software is implemented, and advantages of choosing these implementation tools relative to software are shown. The process of deleting personal data from DICOM files is described in detail; the process of obtaining the “mask” of the object in the picture, which is then used to obtain three-dimensional models of the patient’s internal organs. The process of user work with the database and the search for pathologies using the system interface tools are clearly described. The possibilities of using this information system in the educational field are shown – an illustration of specific clinical cases in order to search for cause-effect relationships in the pathogenesis of various diseases and the development of clinical thinking in a student. In a specific clinical case, an example is given of how the SkiaAtlas program was used to search for a pathology – a volumetric formation of the left hemisphere of the brain.
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Kodenko, Maria R., and Tatiana A. Makarova. "Preparation of abdominal computed tomography data set for patients with abdominal aortic aneurysm." Digital Diagnostics 4, no. 1S (June 26, 2023): 90–92. http://dx.doi.org/10.17816/dd430355.
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BACKGROUND: Artificial intelligence (AI) technologies are actively implemented in the processing and analysis of diagnostic medical images. The accuracy and reliability of AI algorithms are determined by the amount and quality of training data sets. Currently, a need exists for increased open access data sets, particularly abdominal aortic CT angiographic studies (CTA). Limitations of existing abdominal aortic CTA data sets are binary labeling (classification of the entire study) and small number of examinations. In addition, most examinations do not contain signs of aortic pathology, which, given its variability, significantly limits their use for AI training, since the target of such algorithms is the detection of pathology.
AIM: To prepare a CTA data set for patients with abdominal aortic aneurysm.
METHODS: Using the CTA data set with sings of abdominal aortic aneurysm, the stages and features of data set creation for AI training in accordance with the methodological recommendations were considered. Given the basic diagnostic requirements for the selected clinical task, the terms of reference for the preparation of the data set were formed, the required sample size was calculated, and the optimal annotation scenario was determined. The next stage included the selection of initial CT data of abdominal organs in the Unified Radiology Information System, anonymization of data, semi-automatic labeling and of the area of interest (aortic wall and aortic bed) using the 3D Slicer tool and its verification by an examining radiologist, and documentation of intermediate results.
RESULTS: The calculated sample volume included 100 scans, containing the arterial phase, with a slice thickness of up to 1.2 mm. The balance of normal vs. pathology classes was chosen to be 1:4. Partial annotation of the data (50%) was performed.
CONCLUSIONS: A methodology for preparing CTA data sets was developed. The generated dataset, if the necessary procedures are followed, will be placed in the public domain and may be used for training and testing AI algorithms and conducting scientific research.
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Nguyen, My N., Kotori Harada, Takahiro Yoshimoto, Nam Phong Duong, Yoshihiro Sowa, Koji Sakai, and Masayuki Fukuzawa. "Integrated Dataset-Preparation System for ML-Based Medical Image Diagnosis with High Clinical Applicability in Various Modalities and Diagnoses." SN Computer Science 5, no. 6 (June 26, 2024). http://dx.doi.org/10.1007/s42979-024-03025-7.
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AbstractThis study proposed an integrated dataset-preparation system for ML-based medical image diagnosis, offering high clinical applicability in various modalities and diagnostic purposes. With the proliferation of ML-based computer-aided diagnosis using medical images, massive datasets should be prepared. Lacking of a standard procedure, dataset-preparation may become ineffective. Besides, on-demand procedures are locked to a single image-modality and purpose. For these reasons, we introduced a dataset-preparation system applicable for a variety of modalities and purposes. The system consisted of a common part including incremental anonymization and cross annotation for preparing anonymized unprocessed data, followed by modality/subject-dependent parts for subsequent processes. The incremental anonymization was carried out in batch after the image acquisition. Cross annotation enabled collaborative medical specialists to co-generate annotation objects. For quick observation of dataset, thumbnail images were created. With anonymized images, preprocessing was accomplished by complementing manual operations with automatic operations. Finally, feature extraction was automatically performed to obtain data representation. Experimental results on two demonstrative systems dedicated to esthetic outcome evaluation of breast reconstruction surgery from 3D breast images and tumor detection from breast MRI images were provided. The proposed system successfully prepared the 3D breast-mesh closures and their geometric features from 3D breast images, as well as radiomics and likelihood features from breast MRI images. The system also enabled effective voxel-by-voxel prediction of tumor region from breast MRI images using random-forest and k-nearest-neighbors algorithms. The results confirmed the efficiency of the system in preparing dataset with high clinical applicability regardless of the image modality and diagnostic purpose.
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Jabarulla, M. Y., T. Uden, P. Beerbaum, and S. Oeltze-Jafra. "Artificial intelligence in pediatric echocardiography-Automated view classification and image anonymization in rare cardiac malformations on the example of borderline HLHS." European Heart Journal 44, Supplement_2 (November 2023). http://dx.doi.org/10.1093/eurheartj/ehad655.061.
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Abstract Background/Introduction Artificial intelligence (AI) has already much potential in adult echocardiography. However, there is still few experience in how to effectively implement AI methods in pediatric echocardiography. Since children with congenital heart defects show a large anatomical variance, AI-based image analysis tools adapted to this group are necessary. For the fundamentally needed steps of an automatic image data anonymization and a view classification there are still no reliable tools available. Purpose We have developed an anonymization tool for pediatric echocardiography data. For effective automated analysis of echocardiographies in children, we established an algorithm using bHLHS as an example that requires only small numbers of patients for a good view classification accuracy. Thus, we expect that the algorithm will also be applicable to other cardiac malformations. Method Raw data were obtained from visage imaging platforms and echocardiography examinations were conducted on Siemens machines. An anonymization tool was developed using the Python programming language and the Tkinter library. It converts medical images and crops the embedded data from the non-anatomical top region. The classification neural network was evaluated with data obtained from 5 patients with bHLHS. After data cleaning, each patient's image is labelled according to different Echo views: 4CH, SA, and LA, creating a total of 6600 images of 3 separate views. Due to the limited echocardiography dataset, a data augmentation approach was employed to improve the generalization of the AI model. The transfer learning approach was used to develop the AI model, which utilized a pre-trained VGG 13 model that was modified to classify the three different views of the pediatric echocardiography dataset. The modified model was fine-tuned on the dataset with a GPU-enhanced Intel i9 platform and Pycharm 2022.2.4. We use 70% dataset for training, 20% dataset for testing, and 10% dataset for validating. Results Table 1 shows that the expert clinician validated the anonymization tool by comparing identified patient information in the raw image and anonymized image. The trained pediatric echocardiography view classification model achieved an overall accuracy of 0.776 with precision values of 0.725, 0.889, and 0.775 for LA, SA and 4CH classes, respectively. The F1 score values were 0.835, 0.690, and 0.778 for the same classes. ROC curve were plotted (Figure 1) to visualize the performance of the model. Conclusion This study shows the importance of data anonymization and view classification in automating the diagnosis of HLHS using echocardiography images. The developed deep learning algorithm showed promising performance in accurately classifying different views of echocardiography images for diagnosing HLHS. Future studies can build upon this work by developing a complete diagnostic system to automatically analyze cardiac structures of patients with cardiac malformations.
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Kossen, Tabea, Manuel A. Hirzel, Vince I. Madai, Franziska Boenisch, Anja Hennemuth, Kristian Hildebrand, Sebastian Pokutta, et al. "Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks." Frontiers in Artificial Intelligence 5 (May 2, 2022). http://dx.doi.org/10.3389/frai.2022.813842.
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Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.
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Primiero, Clare A., Brigid Betz-Stablein, Nathan Ascott, Brian D’Alessandro, Seraphin Gaborit, Paul Fricker, Abigail Goldsteen, et al. "A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification." Frontiers in Medicine 11 (April 9, 2024). http://dx.doi.org/10.3389/fmed.2024.1380984.
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IntroductionArtificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with ‘untrained’ or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process.MethodsThis protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data.ConclusionThe anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.
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Sahlsten, Jaakko, Kareem A. Wahid, Enrico Glerean, Joel Jaskari, Mohamed A. Naser, Renjie He, Benjamin H. Kann, Antti Mäkitie, Clifton D. Fuller, and Kimmo Kaski. "Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases." Frontiers in Oncology 13 (February 28, 2023). http://dx.doi.org/10.3389/fonc.2023.1120392.
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BackgroundDemand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs).MethodsA publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC).ResultsMost defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively.ConclusionDefacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.
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Kim, Hyeongbok, Zhiqi Pang, Lingling Zhao, Xiaohong Su, and Jin Suk Lee. "Semantic-aware deidentification generative adversarial networks for identity anonymization." Multimedia Tools and Applications, October 7, 2022. http://dx.doi.org/10.1007/s11042-022-13917-6.
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AbstractPrivacy protection in the computer vision field has attracted increasing attention. Generative adversarial network-based methods have been explored for identity anonymization, but they do not take into consideration semantic information of images, which may result in unrealistic or flawed facial results. In this paper, we propose a Semantic-aware De-identification Generative Adversarial Network (SDGAN) model for identity anonymization. To retain the facial expression effectively, we extract the facial semantic image using the edge-aware graph representation network to constraint the position, shape and relationship of generated facial key features. Then the semantic image is injected into the generator together with the randomly selected identity information for de-Identification. To ensure the generation quality and realistic-looking results, we adopt the SPADE architecture to improve the generation ability of conditional GAN. Meanwhile, we design a hybrid identity discriminator composed of an image quality analysis module, a VGG-based perceptual loss function, and a contrastive identity loss to enhance both the generation quality and ID anonymization. A comparison with the state-of-the-art baselines demonstrates that our model achieves significantly improved de-identification (De-ID) performance and provides more reliable and realistic-looking generated faces. Our code and data are available on https://github.com/kimhyeongbok/SDGAN
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Usman Akbar, Muhammad, Måns Larsson, Ida Blystad, and Anders Eklund. "Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models." Scientific Data 11, no. 1 (February 29, 2024). http://dx.doi.org/10.1038/s41597-024-03073-x.
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AbstractLarge annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1–3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%–90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.
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Alzate-Grisales, Jesús Alejandro, Alejandro Mora-Rubio, Harold Brayan Arteaga-Arteaga, Mario Alejandro Bravo-Ortiz, Daniel Arias-Garzón, Luis Humberto López-Murillo, Esteban Mercado-Ruiz, et al. "Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia." Scientific Data 9, no. 1 (December 7, 2022). http://dx.doi.org/10.1038/s41597-022-01576-z.
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AbstractThe emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with “S.E.S Hospital Universitario de Caldas” (https://hospitaldecaldas.com/) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.