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Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

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Journal of Computer and Communications 2021年第7期9卷 8-20页

作者： Andreea Roxana Luca Tudor Florin Ursuleanu Liliana Gheorghe Roxana grigorovici Stefan Iancu Maria Hlusneac Cristina Preda alexandru grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Departament Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania Department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Iasi Departament of Surgery I Regional Institute of Oncology Iasi Romania Department of Radiology “Sf. Spiridon” Hospital Iasi Romania Department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into

关键词： Combined Model of U-Net-Based Architectures Medical Image Segmentation 2D/3D/CT/RMN Images

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Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

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E-Health Telecommunication Systems and Networks 2021年第2期10卷 41-74页

作者： Tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana grigorovici Stefan Iancu Maria Hlusneac Cristina Preda alexandru grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Department of Surgery I Regional Institute of Oncology Iasi Romania Department of Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania Department of Radiology “Sf. Spiridon” Hospital Iasi Romania Department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

关键词： Medical Image Analysis Data Types Labels Deep Learning Models

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The Use of Artificial Intelligence on Colposcopy Images, in the Diagnosis and Staging of Cervical Precancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 266-270页

作者： Andreea Roxana Luca Tudor Florin Ursuleanu Liliana Gheorghe Roxana grigorovici Stefan Iancu Maria Hlusneac Cristina Preda alexandru grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Iasi Department of Surgery I Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition an

关键词： Artificial Intelligence Colposcopy Images Cervical Precancer

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The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 300-304页

作者： Tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana grigorovici Stefan Iancu Maria Hlusneac Cristina Preda alexandru grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Department of Surgery I Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixt

关键词： Artificial Intelligence Cervical Cancer Thyroid Cancer MRI Images CT Images

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