Improving predictive CTV segmentation on CT and CBCT for cervical cancer by diffeomorphic registration of a prior.

PURPOSE: Automatic cervix-uterus segmentation of the clinical target volume (CTV) on CT and cone-beam CT (CBCT) scans is challenged by the limited visibility and the non-anatomical definition of certain border regions. We study the potential performance gain of convolutional neural networks by regulating the segmentation predictions as diffeomorphic deformations of a segmentation prior. MATERIALS AND METHODS: We introduce a 3D convolutional neural network that segments the target scan by joint voxel-wise classification and the registration of a given prior. We compare this network to two other 3D baseline models: One treating segmentation as a classification problem (segmentation-only), the other as a registration problem (deformation-only). For reference and to highlight the benefits of a 3D model, these models are also benchmarked against a 2D segmentation model. Network performances are reported for CT and CBCT segmentation of the cervix-uterus CTV. We train the networks on the data of 84 patients. The prior is provided by the CTV segmentation of a planning CT. Repeat CT or CBCT scans constitute the target scans to be segmented. RESULTS: All 3D models outperformed the 2D segmentation model. For CT segmentation, combining classification and registration in the proposed joint model proved beneficial, achieving a Dice score of 0.87 and a mean squared error (MSE) of the surface distance below 1.7 mm. No such synergy was observed for CBCT segmentation, for which the joint and the deformation-only model performed similarly, achieving a Dice score of about 0.80 and an MSE surface distance of 2.5 mm. However, the segmentation-only model performed notably worse in this low contrast regime. Visual inspection revealed that this performance drop translated into geometric inconsistencies between the prior and target segmentation. Such inconsistencies were not observed for the deformation-based models. CONCLUSION: Constraining the solution space of admissible segmentation predictions to those reachable by a diffeomorphic deformation of the prior proved beneficial as it improved geometric consistency. Especially for CBCT, with its poor soft-tissue contrast, this type of regularization becomes important as shown by quantitative and qualitative evaluation.

A biomechanical finite element model to generate a library of cervix CTVs.

PURPOSE: To generate a series of physiologically plausible cervix CTVs by biomechanically modeling organ deformation as a consequence of bladder filling. This series can serve as planning CTVs for radiotherapy treatment of cervical cancer patients using a library of plans (LoP) strategy. METHODS: The model was constructed based on the full and empty bladder scans of 20 cervical cancer patients, for which the bladder, rectum and the clinical target volume (CTV) of the cervix were delineated. Finite element modeling (FEM) was used to deform empty to full bladder anatomy. This deformation comprised two steps. In the first step, the surfaces of the bladder and rectum of the empty bladder anatomy were explicitly deformed to the full bladder anatomy and imported as enforced displacements into the biomechanical model. These surface displacements cause volumetric deformations of the bladder, rectum and cervix CTV meshes, dictated by their respective elastic properties and the type of contact among them. In the second step, the residual offset between the simulated and target CTV was corrected by an additional thin plate spline warp. Intermediate structural outputs of a linear superposition of the biomechanical and residual warp then constituted the library of CTVs for each patient. The residual warp was minimized by optimizing the FEM parameters over the 20 patients. Finally, the model was tested for nine healthy volunteers for which repeat MR scans were available as the bladder filled from empty to full. Small and large movers were identified depending on the extent of CTV motion, and analyzed separately. The proposed method was compared against the method currently used in our institute, in which intermediate structures are linearly interpolated between full and empty bladder anatomy, using a thin plate spline warp. The comparison metrics used were the ability to preserve CTV volume throughout the deformation, and residual offsets between repeat and library CTV. RESULTS: Optimal model parameters were found to be compatible with published values. While for the current method, the median CTV volume shrunk by 4% for large movers halfway the deformation (and by up to 10% for individual cases), the proposed FEM-based method preserved CTV volumes throughout the deformation. Regional residual errors between repeat and library CTV reduced by up to 3 mm when averaged over the group of large movers. For individual cases this regional error reduction could be as large as 8 mm. CONCLUSIONS: We developed a robust and automatic method to create a patient-specific FEM-based LoP. The FEM-based method resulted in more accurate library of planning CTVs as compared to the current method, with the greatest improvements observed for patients with large CTV motion. The biomechanical model simulates volumetric deformations from empty to full bladder anatomy, paving the way for dose accumulation in an LoP setting.

Pre-clinical experience of an adaptive plan library strategy in radiotherapy of rectal cancer: An inter-observer study.

BACKGROUND AND PURPOSE: The clinical target volume (CTV) in radiotherapy of rectal cancer is subject to large deformations. With a plan library strategy, the treatment may be adapted to these deformations. The purpose of this study was to determine feasibility and consistency in plan selection for a plan library strategy in radiotherapy of rectal cancer. MATERIAL AND METHODS: Thirty rectal cancer patients were included in this retrospective study with in total 150 CBCT scans. A library of CTVs was constructed with in-house built software using population statistics on daily rectal deformations. The library consisted of five plans based on: the original CTV, two larger, and two smaller CTVs. An inter-observer study (study-I) was performed to test the consistency in plan choices between four observers (all RTTs). After five months the observers were asked to re-evaluate (study-II) the same set of scans based on refined guidelines. RESULTS: In study-I the observers reached accordance with the majority choice in 69% of cases. This improved to 87% in study-II. The consensus meeting revealed that inconsistency in choices mainly arose from inadequate instructions, which were later clarified and formulated more accurately. CONCLUSION: Plan selection based on daily CBCT scans for rectal cancer patients is feasible, and can be performed consistently by well-trained RTTs.