OncoPET_DB: A Freely Distributed Database of Realistic Simulated Whole Body 18F-FDG PET Images for Oncology

Tomeï, Sandrine; Reilhac, Anthonin; Visvikis, Dimitris; Boussion, Nicolas; Odet, C.; Giammarile, F.; Lartizien, Carole

doi:10.1109/tns.2009.2034375

Cited by 27 publications

(21 citation statements)

References 31 publications

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“…PET is used for the crucial part of staging and treatment follow-up of lymphoma, due to a higher sensitivity and specificity than anatomical medical imaging modalities. The small lesions mainly localized in lymph nodes indicate lymphoma and lesions can also extend to other organs such as the liver, the spleen, and the lungs [27].…”

Section: Resultsmentioning

confidence: 99%

PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

Devi¹,

Mini²

2016

IJCTE

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Abstract-Positron emission tomography (PET) image segmentation is a difficult task due to low resolution, high contrast, large variability of pathology, high uncertainties in object boundaries and the inherent noise of PET images. A two staged automatic segmentation technique based on Gabor Annulus filtering and region growing is proposed here. Gabor Annulus filtering is applied on preprocessed image and the result is the input for region growing based on automatic seed selection and threshold determination. Unsupervised evaluation schemes are used for appraising the proposed technique. 93.5% of the lesions considered for validation have been obtained correctly in the segmented image.Index Terms-Gabor annulus filtering, intra-inter disparity, intra region uniformity, inter region contrast, positron emission tomography, region growing.

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Section: Resultsmentioning

confidence: 99%

PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

Devi¹,

Mini²

2016

IJCTE

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“…The OncoPET database 2 [198] We defined the ROIs as being representative of PET-CT images, assuming the anatomical ROIs were from CT and the tumour ROIs from PET.…”

Section: Oncopet: Simulated 3d Lymphoma Imagesmentioning

confidence: 99%

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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“…Reihlac et al [4] proposed a cerebral PET database while Tomei at al. [5] created an FDG-PET database for lymphoma based on the PET-SORTEO MC code. In 2005 a large database of emission tomography simulated brain and whole body images was presented [6].…”

Section: Exploitation Of Realistic Computational Anthropomorphic Phanmentioning

confidence: 99%

Exploitation of realistic computational anthropomorphic phantoms for the optimization of nuclear imaging acquisition and processing protocols

Loudos

Papadimitroulas

Kagadis

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Monte Carlo (MC) simulations play a crucial role in nuclear medical imaging since they can provide the ground truth for clinical acquisitions, by integrating and quantifing all physical parameters that affect image quality. The last decade a number of realistic computational anthropomorphic models have been developed to serve imaging, as well as other biomedical engineering applications. The combination of MC techniques with realistic computational phantoms can provide a powerful tool for pre and post processing in imaging, data analysis and dosimetry. This work aims to create a global database for simulated Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) exams and the methodology, as well as the first elements are presented. Simulations are performed using the well validated GATE opensource toolkit, standard anthropomorphic phantoms and activity distribution of various radiopharmaceuticals, derived from literature. The resulting images, projections and sinograms of each study are provided in the database and can be further exploited to evaluate processing and reconstruction algorithms. Patient studies using different characteristics are included in the database and different computational phantoms were tested for the same acquisitions. These include the XCAT, Zubal and the Virtual Family, which some of which are used for the first time in nuclear imaging. The created database will be freely available and our current work is towards its extension by simulating additional clinical pathologies.

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OncoPET_DB: A Freely Distributed Database of Realistic Simulated Whole Body 18F-FDG PET Images for Oncology

Cited by 27 publications

References 31 publications

PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

A graph-based approach for the retrieval of multi-modality medical images

Exploitation of realistic computational anthropomorphic phantoms for the optimization of nuclear imaging acquisition and processing protocols

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