scholarly journals Standardized representation of the LIDC annotations using DICOM

2018 ◽  
Author(s):  
Andriy Fedorov ◽  
Matthew Hancock ◽  
David Clunie ◽  
Mathias Brochhausen ◽  
Jonathan Bona ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Digital Imaging ◽  
Image Database ◽  
General Purpose ◽  
Research Community ◽  
Similar Type ◽  
Lung Imaging ◽  
Ct Scans ◽  
Imaging Data ◽  
Dicom Standard

The Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) conducted a multi-site reader study that produced a comprehensive database of Computed Tomography (CT) scans for over 1000 subjects annotated by multiple expert readers. The result is hosted in the LIDC-IDRI collection of The Cancer Imaging Archive (TCIA). Annotations that accompany the images of the collection are stored using project-specific XML representation. This complicates their reuse, since no general-purpose tools are available to visualize or query those objects, and makes harmonization with other similar type of data non-trivial. To make the LIDC dataset more FAIR (Findable, Accessible, Interoperable, Reusable) to the research community, we prepared their standardized representation using the Digital Imaging and Communications in Medicine (DICOM) standard. This manuscript is intended to serve as a companion to the dataset to facilitate its reuse.

scholarly journals Standardized representation of the LIDC annotations using DICOM

2019 ◽  
Author(s):  
Andrey Fedorov ◽  
Matthew Hancock ◽  
David Clunie ◽  
Mathias Brochhausen ◽  
Jonathan Bona ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Digital Imaging ◽  
Image Database ◽  
General Purpose ◽  
Research Community ◽  
Similar Type ◽  
Lung Imaging ◽  
Ct Scans ◽  
Imaging Data ◽  
Dicom Standard

The Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) conducted a multi-site reader study that produced a comprehensive database of Computed Tomography (CT) scans for over 1000 subjects annotated by multiple expert readers. The result is hosted in the LIDC-IDRI collection of The Cancer Imaging Archive (TCIA). Annotations that accompany the images of the collection are stored using project-specific XML representation. This complicates their reuse, since no general-purpose tools are available to visualize or query those objects, and makes harmonization with other similar type of data non-trivial. To make the LIDC dataset more FAIR (Findable, Accessible, Interoperable, Reusable) to the research community, we prepared their standardized representation using the Digital Imaging and Communications in Medicine (DICOM) standard. This manuscript is intended to serve as a companion to the dataset to facilitate its reuse.

scholarly journals Standardized representation of the LIDC annotations using DICOM

2019 ◽  
Author(s):  
Andrey Fedorov ◽  
Matthew Hancock ◽  
David Clunie ◽  
Mathias Brochhausen ◽  
Jonathan Bona ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Digital Imaging ◽  
Image Database ◽  
General Purpose ◽  
Research Community ◽  
Similar Type ◽  
Lung Imaging ◽  
Ct Scans ◽  
Imaging Data ◽  
Dicom Standard

The Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) conducted a multi-site reader study that produced a comprehensive database of Computed Tomography (CT) scans for over 1000 subjects annotated by multiple expert readers. The result is hosted in the LIDC-IDRI collection of The Cancer Imaging Archive (TCIA). Annotations that accompany the images of the collection are stored using project-specific XML representation. This complicates their reuse, since no general-purpose tools are available to visualize or query those objects, and makes harmonization with other similar type of data non-trivial. To make the LIDC dataset more FAIR (Findable, Accessible, Interoperable, Reusable) to the research community, we prepared their standardized representation using the Digital Imaging and Communications in Medicine (DICOM) standard. This manuscript is intended to serve as a companion to the dataset to facilitate its reuse.

scholarly journals The International Association for the Study of Lung Cancer Early Lung Imaging Confederation

2020 ◽  
pp. 89-99 ◽  
Author(s):  
James L. Mulshine ◽  
Ricardo S. Avila ◽  
Ed Conley ◽  
Anand Devaraj ◽  
Laurie Fenton Ambrose ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
International Association ◽  
Ct Images ◽  
Global Network ◽  
Lung Imaging ◽  
Chronic Obstructive ◽  
Biomedical Data ◽  
Imaging Data ◽  
Quantitative Ct

PURPOSE To improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care. METHODS ELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC’s hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk. RESULTS The goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools. CONCLUSION This initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide.

scholarly journals A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Ct Scans ◽  
Automated Classification ◽  
Imaging Data ◽  
Use Of Data

Abstract The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, $$F_1$$ F 1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

A HUMERAL COORDINATE SYSTEM FOR IN VIVO 3-D KINEMATICS OF THE GLENOHUMERAL JOINT

2009 ◽  
Vol 12 (03) ◽  
pp. 169-174 ◽  
Author(s):  
Hippolite O. Amadi ◽  
Addie Majed ◽  
Roger J. H. Emery ◽  
Anthony M. J. Bull
Keyword(s):  
Computed Tomography ◽  
Standard Deviation ◽  
Proximal Humerus ◽  
Glenohumeral Joint ◽  
The Other ◽  
Ct Scans ◽  
Coordinate Frame ◽  
Imaging Data ◽  
Axis Orientation

The aim of this study was to define axes from clearly identifiable landmarks on the proximal aspect of the humerus and to compare these for reasonable best alternatives to the use of the humeral canal and elbow epicondylar axes to define a humeral coordinate frame (HCF). The elbow epicondylar axis (EC) and six different humeral canal axes (HC) based on varying lengths of humerus were quantified from 21 computed tomography (CT) scans of humeri. Six additional axes were defined using the proximal humerus only. These included a line from the center of a sphere fit on the humeral head to the 3D surface area centroid of the greater tubercle region, (GT). The inclinations of these axes relative to EC were calculated. GT was found to be the most closely aligned to EC (13.4° ± 6.8°). The inclinations of the other axes ranged from 36.3° to 86.8°. The HC axis orientation was found to be insensitive to humeral shaft lengths (variability, within average: 0.6°). This was chosen as one of two axes for the HCF. It was also the most inter-subject related axis to EC with inclination standard deviation of ±1.8°. EC was therefore predicted from this such that if the superior axis [1 0 0] of an image scan is maintained and the humerus rotated to make its quantified HC align superiorly in the direction [0.98 0.01 0.01], then its EC axis lies laterally in the direction [0 0 1]. This study demonstrates that it is possible with confidence to apply an orthogonal coordinate frame to the humerus based on proximal imaging data only.

A Detailed Methodology for FEM Analysis of Long Bones From CT Using Mimics

2017 ◽  
Author(s):  
Reem A. Yassine ◽  
Mohammad Karim Elham ◽  
Samir Mustapha ◽  
Ramsey F. Hamade
Keyword(s):  
Computed Tomography ◽  
Digital Imaging ◽  
3D Model ◽  
Heterogeneous Medium ◽  
Fem Analysis ◽  
Ct Scans ◽  
Long Bones ◽  
Hounsfield Units ◽  
Fem Model ◽  
Tibia Bone

A detailed and experimentally verified methodology is outlined on how to properly process a long tibia bone starting from computed tomography (CT) scans to finite element modeling (FEM). For pre-processing of the bone, CT scan in the form of Digital Imaging and Communications in Medicine (DICOM) files are segmented using Mimics. Next comes assigning gray value Hounsfield Units (HU) of the bone constituents into their cortical and cancellous regions. To have the FEM model arrives at the same mass of that measured experimentally, it was found that cut-off density, cut-off HU, and the utilized number of sub-materials must be considered as varying parameters. The values of these parameters had to be adjusted to properly demarcate cancellous regions from those of the cortical resulting in heterogeneous medium. Next, prior to generating the FEM mesh from the generated 3D model, volume and surface meshes had to be produced. In order to validate the methodology, the modal frequencies of a long tibia bone were experimentally measured. The FEM values of the properly processed CT scans compared favorably with those found experimentally.

SPECIFIC CHARACTERISTICS OF BONE STRUCTURES ON CT-SCANS OF PATIENTS WITH SINONASAL INVERTED PAPILLOMAS

2019 ◽  
Vol 65 (4) ◽  
pp. 590-595
Author(s):  
Arkadiy Naumenko ◽  
Kseniya Sapova ◽  
Oleg Konoplev ◽  
Svetlana Astashchenko ◽  
Igor Chernushevich
Keyword(s):  
Computed Tomography ◽  
Tumor Recurrence ◽  
Inverted Papilloma ◽  
Ct Scans ◽  
Precise Localization ◽  
Sinonasal Inverted Papilloma ◽  
Bone Structures ◽  
Inverted Papillomas

Precise localization and excision of the originating site of a sinonasal inverted papilloma is essential for decreasing tumor recurrence. In this study we evaluated the use of preoperative computed tomography (CT) to pinpoint the attachment/origi-nating sites of the tumor.

scholarly journals Metacarpal Bony Dimensions Related to Headless Compression Screw Sizes

2019 ◽  
Vol 12 (S 01) ◽  
pp. S39-S44
Author(s):  
Michael Okoli ◽  
Kevin Lutsky ◽  
Michael Rivlin ◽  
Brian Katt ◽  
Pedro Beredjiklian
Keyword(s):  
Computed Tomography ◽  
Fracture Fixation ◽  
Ct Scans ◽  
Compression Screw ◽  
Metacarpal Head ◽  
Headless Compression Screw ◽  
Metacarpal Bones ◽  
Headless Compression Screws ◽  
The Mean ◽  
Distal Aspect

Abstract Introduction The purpose of this study is to determine the radiographic dimensions of the finger metacarpals and to compare these measurements with headless compression screws commonly used for fracture fixation. Materials and Methods We analyzed computed tomography (CT) scans of the index, long, ring, and small metacarpal bones and measured the metacarpal length, distance from the isthmus to the metacarpal head, and intramedullary diameter of the isthmus. Metacarpals with previous fractures or hardware were excluded. We compared these dimensions with the size of several commercially available headless screws used for intramedullary fixation. Results A total of 223 metacarpals from 57 patients were analyzed. The index metacarpal was the longest, averaging 67.6 mm in length. The mean distance from the most distal aspect of the metacarpal head to the isthmus was 40.3, 39.5, 34.4, and 31 mm for the index, long, ring, and small metacarpals, respectively. The narrowest diameter of the isthmus was a mean of 2.6, 2.7, 2.3, and 3 mm for the index, long, ring, and small metacarpals, respectively. Of 33 commercially available screws, only 27% percent reached the isthmus of the index metacarpal followed by 42, 48, and 58% in the long, ring, and small metacarpals, respectively. Conclusion The index and long metacarpals are at a particular risk of screw mismatch given their relatively long lengths and narrow isthmus diameters.

scholarly journals Conditional CT Strategy—An Effective Tool to Reduce Negative Appendectomy Rate and the Overuse of the CT

2021 ◽  
Vol 10 (11) ◽  
pp. 2456
Author(s):  
Raminta Luksaite-Lukste ◽  
Ruta Kliokyte ◽  
Arturas Samuilis ◽  
Eugenijus Jasiunas ◽  
Martynas Luksta ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Acute Appendicitis ◽  
Sensitivity And Specificity ◽  
Surgical Outcomes ◽  
Imaging Modality ◽  
Ct Scans ◽  
Negative Appendectomy ◽  
Transabdominal Ultrasound ◽  
First Choice ◽  
Negative Appendectomy Rate

(1) Background: Diagnosis of acute appendicitis (AA) remains challenging; either computed tomography (CT) is universally used or negative appendectomy rates of up to 30% are reported. Transabdominal ultrasound (TUS) as the first-choice imaging modality might be useful in adult patients to reduce the need for CT scans while maintaining low negative appendectomy (NA) rates. The aim of this study was to report the results of the conditional CT strategy for the diagnosis of acute appendicitis. (2) Methods: All patients suspected of acute appendicitis were prospectively registered from 1 January 2016 to 31 December 2018. Data on their clinical, radiological and surgical outcomes are presented. (3) Results: A total of 1855 patients were enrolled in our study: 1206 (65.0%) were women, 649 (35.0%) were men, and the median age was 34 years (IQR, 24.5–51). TUS was performed in 1851 (99.8%) patients, and CT in 463 (25.0%) patients. Appendices were not visualized on TUS in 1320 patients (71.3%). Furthermore, 172 (37.1%) of 463 CTs were diagnosed with AA, 42 (9.1%) CTs revealed alternative emergency diagnosis and 249 (53.8%) CTs were normal. Overall, 519 (28.0%) patients were diagnosed with AA: 464 appendectomies and 27 diagnostic laparoscopies were performed. The NA rate was 4.2%. The sensitivity and specificity for TUS and CT are as follows: 71.4% and 96.2%; 93.8% and 93.6%. (4) Conclusion: A conditional CT strategy is effective in reducing NA rates and avoids unnecessary CT in a large proportion of patients. Observation and repeated TUS might be useful in unclear cases.

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