419 research outputs found

    A Survey on Deep Learning in Medical Image Analysis

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    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

    CLASSIFICATION OF TAGGED MATERIAL IN A SET OF TOMOGRAPHIC IMAGES OF COLORECTAL REGION

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    method of classification of image portions corresponding to faecal residues from a tomographic image of a colorectal region, which comprises a plurality of voxels (2) each having a predetermined intensity value and which shows at least one portion of colon (6a, 6b, 6c, 6d) comprising at least one area of tagged material (10). The area of tagged material (10) comprises at least one area of faecal residue (10a) and at least one area of tissue affected by tagging (10b). The image further comprises at least one area of air (8) which comprises an area of pure air (8a) not influenced by the faecal residues. The method comprises the operations of identifying (100), on the basis of a predetermined identification criterion based on the intensity values, above-threshold connected regions comprising connected voxels (2) and identifying, within the above-threshold connected regions, a plurality of connected regions of tagged material comprising voxels (2) representing the area of tagged material (10). The method further comprises the operation of classifying (104) each plurality of connected regions of tagged material on the basis of specific classification comparison criteria for each connected region, in such a way as to identify voxels (20) corresponding to the area of faecal residue (10a) and voxels (2) corresponding to the area of tissue affected by tagging (10b)

    Method of classification of tagged material in a set of tomographic images of colorectal region

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    A method of classification of image portions corresponding to fecal residues from a tomographic image of a colorectal region, which comprises a plurality of voxels (2) each having a predetermined intensity value and which shows at least one portion of colon (6 a, 6 b, 6 c, 6 d) comprising at least one area of tagged material (10). The area of tagged material (10) comprises at least one area of fecal residue (10 a) and at least one area of tissue affected by tagging (10 b). The image further comprises at least one area of air (8) which comprises an area of pure air (8 a) not influenced by the fecal residues. The method comprises the operations of identifying (100), on the basis of a predetermined identification criterion based on the intensity values, above-threshold connected regions comprising connected voxels (2) and identifying, within the above-threshold connected regions, a plurality of connected regions of tagged material comprising voxels (2) representing the area of tagged material (10). The method further comprises the operation of classifying (104) each plurality of connected regions of tagged material on the basis of specific classification comparison criteria for each connected region, in such a way as to identify voxels (20) corresponding to the area of fecal residue (10 a) and voxels (2) corresponding to the area of tissue affected by tagging (10 b)

    Virtual colon unfolding for polyp detection

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    Master'sMASTER OF ENGINEERIN

    Tooth Transplantation Using Computer-Aided Rapid Prototyping Model Compared to Conventional Technique (A Pilot Study)

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    Objective: This research study aimed to compare the efficiency between tooth transplantation using the Computer-aided rapid prototyping model (CARP model) and a conventional tooth transplantation technique. Materials & Methods: Ten patients were enrolled in this study. Patients were randomly divided into 2 groups. Five patients were performed tooth transplantation using the CARP technique (study group) and other five patients were performed antotransplantation using the conventional technique (controlled group). During transplantation, operation time, extra-alveolar time, and attempt of fitting donor tooth to recipient site were evaluated. Moreover, after 3 months post-operation, PDL space, tooth mobility, and pocket depth were examined. Result: During transplantation, the study group consumed lower operating time and extraalveolar time compared to the control group although no statistic significance was found (p = 0.086 and p = 0.05 respectively). In addition, the study group showed significantly fewer attempts to fit the donor tooth to the recipient socket compared to the control group (p = 0.019). After 3 months post-transplantation, average PDL width shows a narrower significant difference in the study group compared to the control group (p = 0.014). Moreover, the study group showed significantly better pocket depth reduction compared to the control group (p = 0.024). No significant difference found in tooth mobility after tooth transplantation in both groups (p = 0.074). Conclusion: CARP technique reduced attempt to fitting donor tooth and improved PDL healing of donor tooth in tooth transplantation compared to conventional technique

    Application of artificial intelligence in the dental field : A literature review

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    Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning

    Data efficient deep learning for medical image analysis: A survey

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    The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie
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