Approximate Vertebral Body Instance Segmentation by PET-CT Fusion for Assessment After Hematopoietic Stem Cell Transplantation

We introduce a new, fully automatic vertebral instance segmentation method to facilitate the extraction of standard uptake values (SUV) from the medullary cavities of individual vertebral bodies in joint 18 F-fluorothymidine (FLT) PET/CT scans acquired from hematopoietic stem cell transplantation (HSCT) patients at 28 days after transplant. Due to dosing considerations, the CT voxels in these scans are characterized by a large 5 mm axial slice thickness which significantly complicates the vertebral body segmentation problem. The key ideas of our method are to first apply an ensemble of U-Nets to obtain a binary mask for the aggregated collection of vertebral bodies as a single object without estimating the intervertebral boundaries, and then leverage the relatively better 4 mm axial slice spacing in the PET data to estimate a “best fit” axial coordinate to approximate the break between each pair of vertebrae. This PET-CT fusion approach results in an approximate vertebral body segmentation where each estimated intervertebral boundary is, by construction, restricted to lie in a single axial plane. However, because the FLT uptake is well localized within the medullary cavities, our results show that this approximate segmentation is sufficiently accurate to enable FLT SUV data to be isolated for individual vertebral bodies. Compared to traditional methods for assessing engraftment based on single aspirate biopsies, this new technique has potential to facilitate a significantly more comprehensive assessment of the medullary compartment by providing fully automated SUV data for a plurality of individual bones.