EP-1887: Automated 3D MRI pancreas segmentation
Ke Sheng, Shuiping Gou, P. Hu · Radiotherapy and Oncology · 2016
_____________________________________________________________________________________________________ levels, and to compare the segmentation accuracy with CTbased autosegmentation.Material and Methods: 14 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 immobilisation 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 (In Idx).Results: Figure 1 illustrates example manual and autocontours generated on the CT and MRI scans.Automatic segmentation using MRI of orbits, parotids, brainstem and lymph node level was acceptable with a DICE coefficient of 0.73-0.91,MDC 2.0-5.1mmSe Idx.0.64-0.93,In 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.