The feasibility of atlas-based automatic segmentation of MRI for H & N radiotherapy planning

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 segment ation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple position...
Source: Journal of Applied Clinical Medical Physics - Category: Physics Source Type: research