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@ARTICLE{Huang:299588,
      author       = {L. Huang and A. Thummerer and C. I. Papadopoulou and S.
                      Corradini and C. Belka$^*$ and M. Riboldi and C. Kurz and G.
                      Landry},
      title        = {{V}alidation of patient-specific deep learning markerless
                      lung tumor tracking aided by 4{DCBCT}.},
      journal      = {Physics in medicine and biology},
      volume       = {70},
      number       = {6},
      issn         = {0031-9155},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2025-00529},
      pages        = {065001},
      year         = {2025},
      abstract     = {Objective. Tracking tumors with multi-leaf collimators and
                      x-ray imaging can be a cost-effective motion management
                      method to reduce internal target volume margins for lung
                      cancer patients, sparing normal tissues while ensuring
                      target coverage. To realize that, accurate tumor
                      localization on x-ray images is essential. We aimed to
                      develop a systematic method for automatically generating
                      tumor segmentation ground truth (GT) on cone-beam computed
                      tomography (CBCT) projections and use it to help refine and
                      validate our patient-specific AI-based tumor localization
                      model.Approach. To obtain the tumor segmentation GT on CBCT
                      projections, we propose a 4DCBCT-aided GT generation
                      pipeline consisting of three steps: breathing phase
                      extraction and 10-phase 4DCBCT reconstruction, manual
                      segmentation on phase $50\%$ followed by deformable contour
                      propagation to other phases, and forward projection of the
                      3D segmentation to the CBCT projection of the corresponding
                      phase. We then used the CBCT projections from one fraction
                      in the angular range of [-10∘, 10∘] and [80∘, 100∘]
                      to refine a Retina U-Net baseline model, which was
                      pretrained on 1140231 digitally reconstructed radiographs
                      generated from a public lung dataset for automatic tumor
                      delineation on projections, and used later-fraction CBCT
                      projections in the same angular range for testing. Six LMU
                      University Hospital patient CBCT projection sets were
                      reserved for validation and 11 for testing. Tracking
                      accuracy was evaluated as the center-of-mass (COM) error and
                      the Dice similarity coefficient (DSC) between the predicted
                      and ground-truth segmentations.Main results. Over the 11
                      testing patients, each with around 40 CBCT projections
                      tested, the patient refined models had a mean COM error of
                      2.3 ± 0.9 mm/4.2 ± 1.7 mm and a mean DSC of 0.83 ±
                      0.06/0.72 ± 0.13 for angles within [-10∘, 10∘] /
                      [80∘, 100∘]. The mean inference time was 68 ms/frame.
                      The patient-specific training segmentation loss was found to
                      be correlated to the segmentation performance at [-10∘,
                      10∘].Significance. Our proposed approach allows
                      patient-specific real-time markerless lung tumor tracking,
                      which could be validated thanks to the novel 4DCBCT-aided GT
                      generation approach.},
      keywords     = {Lung Neoplasms: diagnostic imaging / Humans / Deep Learning
                      / Cone-Beam Computed Tomography: methods / Four-Dimensional
                      Computed Tomography: methods / Image Processing,
                      Computer-Assisted: methods / 4DCBCT (Other) / AI (Other) /
                      lung tumor targeting (Other) / markerless tracking (Other) /
                      patient-specific (Other)},
      cin          = {MU01},
      ddc          = {530},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39978071},
      doi          = {10.1088/1361-6560/adb89c},
      url          = {https://inrepo02.dkfz.de/record/299588},
}