419 research outputs found
A Survey on Deep Learning in Medical Image Analysis
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
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
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)
Tooth Transplantation Using Computer-Aided Rapid Prototyping Model Compared to Conventional Technique (A Pilot Study)
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
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
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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Visualisation of curved tubular structures in medical databases: An application to virtual colonoscopy
Medical conditions affecting the colon are problematic to diagnose due to the difficulty in examining this particular internal organ. To date, the most widely used approach is to perform a colonoscopy; a procedure in which a small camera is inserted into the colon to examine its surface. This procedure is unpleasant and potentially dangerous for the patient, and is expensive and time consuming for the hospital. As a result, patients at risk of developing the conditions are not always screened as often as would be desirable.
Over the last few years a new approach known as virtual colonoscopy has been gaining popularity. The method uses information from a CT scan to reconstruct a 3D model of the colon which can then be examined without the patient needing to undergo a colonoscopy. This approach is now commonly used when screening for polyps (an indication of colon cancer) but can not be so easily used on conditions such as Inflammatory Bowel Disease (IBD) where information beyond the shape of the surface is required.
This thesis forms part of a larger project which aims to diagnose conditions such as IBD by using image processing algorithms on CT data and presenting the results to the user in an easy to interpret way. Specifically we are concerned with this visualisation stage of the system and so have developed a new visualisation approach which we call Volumetric CPR. This can be used to supplement the more traditional virtual flythrough visualisation and is applicable to IBD detection as well as screening for polyps.
Our technique builds on the concept of Curved Planar Reformation (CPR), which has proved to be a practical and widely used tool for the visualisation of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for structures such as the colon because abnormalities are smaller relative to the size of the structure and may not have such distinct density and shape characteristics.
Our new approach improves on this situation by using volume rendering for hollow regions of the structure and standard CPR, for the surrounding tissue. This effectively combines grey scale contextual information with detailed colour information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative for virtual colonoscopy.
We also demonstrate how systems can effectively utilize this new visualisation in order to convey maximum information to the user. We show how overlays can be used to present surface coverage data and how sophisticated lighting models can improve the users understanding of the 3D structure. We also present details of how to integrate our visualisation into existing systems and work flows
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