6533b836fe1ef96bd12a15b4
RESEARCH PRODUCT
A smart and operator independent system to delineate tumours in Positron Emission Tomography scans
Albert Comelli23Alessandro StefanoGiorgio Russo 14Maria Gabriella Sabini 4Massimo Ippolito 5Samuel Bignardi 3Giovanni PetrucciAnthony Yezzi 3subject
Lung NeoplasmsComputer sciencemedicine.medical_treatmentPET imagingPattern Recognition Automated030218 nuclear medicine & medical imaging0302 clinical medicineNeoplasmsImage Processing Computer-AssistedSegmentationDiagnosis Computer-AssistedNeoplasm MetastasisRadiation treatment planningSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniObserver VariationActive contour modelmedicine.diagnostic_testBrain NeoplasmsPhantoms ImagingComputer Science ApplicationsHead and Neck NeoplasmsPositron emission tomography030220 oncology & carcinogenesis18F-fluoro-2-deoxy-d-glucoseAlgorithms18F-fluoro-2-deoxy-d-glucose and 11C-labeled methionine PET imagingSimilarity (geometry)Health InformaticsSensitivity and SpecificityNOActive contour algorithm03 medical and health sciencesFluorodeoxyglucose F18Predictive Value of TestsRegion of interestmedicineHumansFalse Positive ReactionsRetrospective Studies18F-fluoro-2-deoxy-d-glucose 11C-labeled methionine PET imaging Active contour algorithm Biological target volume Cancer segmentationbusiness.industryRadiotherapy Planning Computer-Assisted11C-labeled methionineReproducibility of ResultsPattern recognitionGold standard (test)Cancer segmentationRadiation therapyBiological target volumePositron-Emission TomographyArtificial intelligenceTomography X-Ray ComputedbusinessSoftwaredescription
Abstract 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 R2 = 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 | Computers in Biology and Medicine |