6533b870fe1ef96bd12cf2a3
RESEARCH PRODUCT
Head–Neck Cancer Delineation
Roberto Pirrone 6Enrico Antonio Lo FasoOrazio Gambinosubject
medicine.medical_specialtyComputer sciencemedicine.medical_treatmentImage processinghead–neck cancer (HNC)Head neck cancerlcsh:Technology030218 nuclear medicine & medical imagingTask (project management)head and neck squamous cell carcinoma (HNSCC)lcsh:Chemistry03 medical and health sciencestumor delineation0302 clinical medicinemedicineGeneral Materials ScienceMedical physicsSegmentationlcsh:QH301-705.5InstrumentationSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniFluid Flow and Transfer Processesmedicine.diagnostic_testlcsh:Tbusiness.industryProcess Chemistry and TechnologyDeep learningsegmentationGeneral EngineeringCT Head and neck squamous cell carcinoma (HNSCC) Head–neck cancer (HNC) MRI Nasopharyngeal cancer (NPC) PET Segmentation Tumor delineationnasopharyngeal cancer (NPC)lcsh:QC1-999Computer Science ApplicationsRadiation therapylcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Positron emission tomography030220 oncology & carcinogenesisArtificial intelligenceTomographylcsh:Engineering (General). Civil engineering (General)businesslcsh:PhysicsCTdescription
Head–Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PET-Computerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories on the web. The adopted techniques can belong to the well-known image processing/computer-vision algorithms or the newest deep learning/artificial intelligence approaches. All these aspects are analyzed in the present review and comparison among various approaches is performed. From the present review, the authors draw the conclusion that despite the encouraging results of computerized approaches, their performance is far from handmade tumor delineation result.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-03-18 | Applied Sciences |