title: The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.
abstract: Atlas-based autosegmentation is an established tool for segmenting structures for CT-planned head and neck radiotherapy. MRI is being increasingly integrated into the planning process. The aim of this study is to assess the feasibility of MRI-based, atlas-based autosegmentation for organs at risk (OAR) and lymph node levels, and to compare the segmentation accuracy with CT-based autosegmentation. Fourteen patients with locally advanced head and neck cancer in a prospective imaging study underwent a T1-weighted MRI and a PET-CT (with dedicated contrast-enhanced CT) in an immobilization mask. Organs at risk (orbits, parotids, brainstem, and spinal cord) and the left level II lymph node region were manually delineated on the CT and MRI separately. A 'leave one out' approach was used to automatically segment structures onto the remaining images separately for CT and MRI. Contour comparison was performed using multiple positional metrics: Dice index, mean distance to conformity (MDC), sensitivity index (Se Idx), and inclusion index (Incl Idx). Automatic segmentation using MRI of orbits, parotids, brainstem, and lymph node level was acceptable with a DICE coefficient of 0.73-0.91, MDC 2.0-5.1mm, Se Idx 0.64-0.93, Incl Idx 0.76-0.93. Segmentation of the spinal cord was poor (Dice coefficient 0.37). The process of automatic segmentation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple positional metrics; spinal cord segmentation based on MRI was inferior compared with CT. Accurate atlas-based automatic segmentation of OAR and lymph node levels is feasible using T1-MRI; segmentation of the spinal cord was found to be poor. Comparison with CT-based automatic segmentation suggests that the process is equally as, or more accurate, using MRI. These results support further translation of MRI-based segmentation methodology into clinicalpractice.
authors.affiliation: University of Leeds. um11kcw@leeds.ac.uk.
authors.forename: Kieran
authors.initials: K
authors.lastname: Wardman
authors.forename: Robin J D
authors.initials: RJ
authors.lastname: Prestwich
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Gooding
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Speight
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D001330:Electronic Data Processing / Q000379:methods*; D005240:Feasibility Studies; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008953:Models, Anatomic; D009367:Neoplasm Staging; D058958:Organs at Risk / Q000528:radiation effects; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods