3 research outputs found
Multiclass Bone Segmentation of PET/CT Scans for Automatic SUV Extraction
In this thesis I present an automated framework for segmentation of bone
structures from dual modality PET/CT scans and further extraction of SUV
measurements. The first stage of this framework consists of a variant of the
3D U-Net architecture for segmentation of three bone structures: vertebral
body, pelvis, and sternum. The dataset for this model consists of annotated
slices from the CT scans retrieved from the study of post-HCST patients and
the 18F-FLT radiotracer, which are undersampled volumes due to the low-dose
radiation used during the scanning. The mean Dice scores obtained by the
proposed model are 0.9162, 0.9163, and 0.8721 for the vertebral body, pelvis,
and sternum class respectively. The next step of the proposed framework
consists of identifying the individual vertebrae, which is a particularly difficult
task due to the low resolution of the CT scans in the axial dimension. To
address this issue, I present an iterative algorithm for instance segmentation
of vertebral bodies, based on anatomical priors of the spine for detecting the
starting point of a vertebra. The spatial information contained in the CT and
PET scans is used to translate the resulting masks to the PET image space and
extract SUV measurements. I then present a CNN model based on the
DenseNet architecture that, for the first time, classifies the spatial distribution
of SUV within the marrow cavities of the vertebral bodies as normal
engraftment or possible relapse. With an AUC of 0.931 and an accuracy of 92%
obtained on real patient data, this method shows good potential as a future
automated tool to assist in monitoring the recovery process of HSCT patients