| dc.description.abstract |
<p>Deformable image registration (DIR) is the process of registering two or more images
to a reference image by minimizing local differences across the entire image. DIR
is conventionally performed using iterative optimization-based methods, which are
time-consuming and require manual parameter tuning. Recent studies have shown that
deep learning methods, most importantly convolutional neural networks (CNNs), can
be employed to address the DIR problem. In this study, we propose two deep learning
frameworks to perform the DIR task in an unsupervised approach for CT-to-CT deformable
registration of the head and neck region. Given that head and neck cancer patients
might undergo severe weight loss over the course of their radiation therapy treatment,
DIR in this region becomes an important task. The first proposed deep learning framework
contains two scales, where both scales are based on freeform deformation, and are
trained based on minimizing a dissimilarity intensity-based metrics, while encouraging
the deformed vector field (DVF) smoothness. The two scales were first trained separately
in a sequential manner, and then combined in a two-scale joint training framework
for further optimization. We then developed a transfer learning technique to improve
the DIR accuracy of the proposed deep learning networks by fine-tuning a pre-trained
group-based model into a patient-specific model to optimize its performance for individual
patients. We showed that by utilizing as few as two prior CT scans of a patient, the
performance of the pretrained model described above can be improved yielding more
accurate DIR results for individual patients. The second proposed deep learning framework,
which also consists of two scales, is a hybrid DIR method using B-spline deformation
modeling and deep learning. In the first scale, deformation of control points are
learned by deep learning and initial DVF is estimated using B-spline interpolation
to ensure smoothness of the initial estimation. Second scale model of the second framework
is the same as that in the first framework. In our study, the networks were trained
and evaluated using public TCIA HNSCC-3DCT for the head and neck region. We showed
that our DIR results of our proposed networks are comparable to conventional DIR methods
while being several orders of magnitude faster (about 2 to 3 seconds), making it highly
applicable for clinical applications.</p>
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