A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.