An Automatic Method for Metabolic Evaluation of Gamma Knife Treatments

Stefano, Alessandro; Vitabile, Salvatore; Russo, Giorgio; Ippolito, Massimo; Marletta, Franco; D’Arrigo, Corrado; D’Urso, Diego; Sabini, M.G.; Gambino, Orazio; Pirrone, Roberto; Ardizzone, Edoardo; Gilardi, M.C.

doi:10.1007/978-3-319-23231-7_52

Cited by 13 publications

(15 citation statements)

References 17 publications

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“…It is for this reason that the combination of architecture plus agent system can be adapted and extended to other and more complicated clinical scenarios. For instance, specialized agents could support specialized physicians through intelligent software that uses a data-driven approach for building interfaces as in [55] or including techniques for data manipulation as in [56,57]. Moreover, everything related to monitoring can be deployed in various sensors to adopt IoT techniques for patient monitoring.…”

Section: Discussionmentioning

confidence: 99%

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Lanza

Seidita

Chella

2020

Journal of Biomedical Informatics

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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Section: Discussionmentioning

confidence: 99%

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Lanza

Seidita

Chella

2020

Journal of Biomedical Informatics

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“…Although the high contrast between tumor and normal tissue on PET images can reduce the inter-and intra-observer variability in tumor localization, the variability in tumor delineation with the qualitative use of PET is still high and often inconsistent with anatomically defined GTV [228]. Due to the nature of PET images (low spatial resolution, high noise and weak boundary), the Biological Target Volume (BTV) varies substantially depending on the algorithm used to segment functional lesions: the choice of a standard method for PET volume contouring is a very challenging yet unresolved step [585]. Anatomical GTV often does not match with metabolic BTV at all.…”

Section: Biomedical Image Analysis and Computingmentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

102

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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“…Each PET slice consists of 256 × 256 voxels with a grid spacing of 2.73 mm 3 and thickness of 3.27 mm 3 . Consequently, the size of each voxel is 2.73 × 2.73 × 3.27 mm 3 .…”

Section: Pet/ct Acquisition Protocolmentioning

confidence: 99%

“…Positron Emission Tomography (PET) is a non-invasive medical imaging technique which has the advantage over other anatomical imaging techniques, such as Computerized Tomography (CT) and Magnetic Resonance (MR) of providing direct information about patient's functional processes. Metabolic indicators, and in general parameters derived from PET imaging, might be predictive of patient therapy response to the pharmacological treatment of cancer [1] and are useful in obtaining an objective evaluation of the changes in the patient condition [2,3]. As a matter of fact, metabolic parameters are often faster changing and more indicative of therapy effects than morphological changes [2].…”

Section: Introductionmentioning

confidence: 99%

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

Comelli

Stefano

Russo

et al. 2018

Computers in Biology and Medicine

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Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.

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An Automatic Method for Metabolic Evaluation of Gamma Knife Treatments

Cited by 13 publications

References 17 publications

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Agents and robots for collaborating and supporting physicians in healthcare scenarios

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

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