title: A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.
abstract: 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.
authors.affiliation: Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.
authors.forename: Spencer
authors.initials: S
authors.lastname: Martin
authors.forename: Mark
authors.initials: M
authors.lastname: Brophy
authors.forename: David
authors.initials: D
authors.lastname: Palma
authors.forename: Alexander V
authors.initials: AV
authors.lastname: Louie
authors.forename: Edward
authors.initials: E
authors.lastname: Yu
authors.forename: Brian
authors.initials: B
authors.lastname: Yaremko
authors.forename: Belal
authors.initials: B
authors.lastname: Ahmad
authors.forename: John L
authors.initials: JL
authors.lastname: Barron
authors.forename: Steven S
authors.initials: SS
authors.lastname: Beauchemin
authors.forename: George
authors.initials: G
authors.lastname: Rodrigues
authors.forename: Stewart
authors.initials: S
authors.lastname: Gaede
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology / Q000532:radiotherapy*; D032921:Consensus; D056973:Four-Dimensional Computed Tomography / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D047368:Tumor Burden