Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images

Huang, Yixuan; Jones, Craig K.; Zhang, X.; Johnston, Ashley; Waktola, S.; Aygün, Nafi; Witham, Timothy F.; Bydon, Ali; Theodore, Nicholas; Helm, Patrick A.; Siewerdsen, Jeffrey H.; Uneri, Ali

doi:10.1016/j.cmpb.2022.107222

Cited by 2 publications

(1 citation statement)

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“…Recognizing the potential for deformations, we will employ a locally rigid registration approach (for each vertebra) to account for changes in spinal curvature. 8 This approach will be combined with automatic initialization using CNN-based vertebrae labeling presented in our early work 9,10 to extend the capture range, parallelize registrations, and reduce algorithm runtime.…”

Section: Discussionmentioning

confidence: 99%

Multi-modality registration of preoperative MR and intraoperative long-length tomosynthesis using GAN synthesis and 3D-2D registration

Huang

Zhang

Johnston

et al. 2023

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

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A new imaging modality (viz., Long-Film [LF]) for acquiring long-length tomosynthesis images of the spine was recently enabled on the O-arm™ system and used in an IRB-approved clinical study at our institution. The work presented here implements and evaluates a combined image synthesis and registration approach to solve multi-modality registration of MR and LF images. The approach is well-suited for pediatric cases that use MR for preoperative diagnosis and aim for lower levels of intraoperative radiation exposure. A patch-based conditional GAN was used to synthesize 3D CT images from MR. The network was trained on deformably co-registered MR and CT image pairs. Synthesized images were registered to LF images using a model-based 3D-2D registration algorithm. Images from our clinical study were manually labeled, and the intra-user variability in anatomical landmark definition was measured in a simulation study. Geometric accuracy of registrations was evaluated on anatomical landmarks in separate test cases from the clinical study. The synthesis process generated CT images with clear bone structures. Analysis of manual labeling revealed 3.1±2.2 mm projection distance error between 3D and 2D anatomical landmarks. Anatomical MR landmarks projected on lateral LF images demonstrated a median projection distance error of 3.6 mm after registration. This work constitutes the first reported approach for MR to LF registration based on deep image synthesis. Preliminary results demonstrated the feasibility of globally rigid registration in aligning preoperative MR and intraoperative LF images. Work currently underway extends this approach to vertebra level, locally rigid / globally deformable registrations, with initialization based on automatically labeled vertebrae levels.

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Section: Discussionmentioning

confidence: 99%