6533b858fe1ef96bd12b5b02
RESEARCH PRODUCT
An enhanced random walk algorithm for delineation of head and neck cancers in PET studies
Alessandro Stefano2Salvatore VitabileGiorgio Russo 14Massimo Ippolito 5Maria Gabriella Sabini 4Daniele Sardina 4Orazio Gambino 6Roberto Pirrone 6Edoardo Ardizzone 6Maria Carla Gilardi 1subject
Similarity (geometry)Computer sciencePET imagingBiomedical EngineeringRandom walk030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinemedicineImage Processing Computer-AssistedHumansSegmentationComputer visionCluster analysisEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPhantoms ImagingBiological target volume; Head and neck cancer segmentation; PET imaging; Random walksComputer Science ApplicationPattern recognitionRandom walkComputer Science ApplicationsBiological target volumeHausdorff distancePositron emission tomographyHead and Neck Neoplasms030220 oncology & carcinogenesisPositron-Emission TomographyArtificial intelligenceHead and neck cancer segmentationComputer Vision and Pattern RecognitionbusinessAlgorithmsBiological target volume Head and neck cancer segmentation PET imaging Random walks Algorithms Head and Neck Neoplasms Humans Image Processing Computer-Assisted Phantoms Imaging Positron-Emission TomographyVolume (compression)description
An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |