The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Clark, Kenneth W.; Vendt, Bruce A.; Smith, Kirk E.; Freymann, John; Kirby, Justin; Koppel, Paul; Moore, Stephen; Phillips, Stanley R.; Maffitt, David R.; Pringle, Michael; Tarbox, Lawrence; Prior, Fred

doi:10.1007/s10278-013-9622-7

Cited by 3,614 publications

(2,234 citation statements)

References 12 publications

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“…The dataset used in this study is from the Cancer Imaging Archive (TCIA) [32,33]. The data were originally annotated and labeled by experts from Thomas Jefferson University and Henry Ford Hospitals.…”

Section: Datasetmentioning

confidence: 99%

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

et al. 2017

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Abstract:In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

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Section: Datasetmentioning

confidence: 99%

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

et al. 2017

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“…The patient had his arms positioned above the head for the pre-treatment case and positioned down for the post-treatment case, respectively. The data set used originates from The Cancer imaging Archive (TCIA) [2], obtained from the Head-Neck Cetuximab collection [3]. Image resolution of both CT scans is 1.37 x 1.37 x 3.75 mm.…”

Section: Patient Datamentioning

confidence: 99%

Handling images of patient postures in arms up and arms down position using a biomechanical skeleton model

Teske

Bartelheimer

Bendl

et al. 2017

Current Directions in Biomedical Engineering

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Deformable image registration is gradually becoming the tool of choice for motion extraction during adaptive radiotherapy. Achieving a motion vector field that accurately represents the anatomical changes requires a tissue specific transformation model. Therefore, widely used spline based models most likely fail in appropriately reproducing large anatomical changes such as the arms of the patient being positioned up and down. We present the application of a tissue specific biomechanical model with the goal to mimic patient motion even in presence of large motion. Based on the planning CT, delineated bones are used to represent the rigid anatomy of the patient. We implement ball-and-socket joints between corresponding bones in order to achieve mobility of the skeleton. An inverse kinematics approach enables the propagation of motion between individual bones across their joints, leading to an articulated skeleton that can be controlled by feature points on one or more bones. The transformation of each bone initializes a chainmail based soft tissue model to also propagate the motion into the surrounding heterogeneous soft tissue. Representation of different postures like arms up and down can be achieved within less than 1 s for the skeleton and ~10 s for the soft tissue.Especially for large anatomical changes, the kinematics approach benefits from the direct articulation at specific joints, considerably lowering the degrees of freedom for motion description. Being the input for the chainmail based soft tissue model, the transformed bones guarantee for its meaningful initialization. The proposed biomechanical skeleton model is promising to facilitate the registration of patients' anatomy, being positioned with arms up and arms down. The results encourage further refinement of the joints and the soft tissue model.

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“…Available imaging data includes computed tomography, magnetic resonance and other images. De-identification scripts support moving more and more images on public servers [24].…”

Section: Open Data In Cancer Researchmentioning

confidence: 99%

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

Jeanquartier

Jean-Quartier

Schreck

et al. 2016

Information Technology in Bio- And Medical Informatics

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Abstract. The general disease group of malignant neoplasms depicts one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to the disease. Our idea is to create a user interface for linking a simulation on cancer modeling to relevant additional publicly and freely available data. We are not only providing a categorized list of open datasets and queryable databases for the different types of cancer and related information, we also identify a certain subset of temporal and spatial data related to tumor growth. Furthermore, we describe the integration possibilities into a simulation tool on tumor growth that incorporates the tumor's kinetics.

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The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Cited by 3,614 publications

References 12 publications

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

Handling images of patient postures in arms up and arms down position using a biomechanical skeleton model

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

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