Abstract
The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible atlas-to-image transformations and compute the expected label value (ELV), thereby not relying merely on the transformation deemed “optimal” by the registration method. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to brain, liver, and pancreas segmentation on datasets of magnetic resonance and computed tomography images.
Competing Interest Statement
B. Fischl has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. B. Fischl's interests were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham according to their conflict of interest policies.
Footnotes
Aganj (iaganj{at}mgh.harvard.edu, +1-617-724-5652) and B. Fischl (bfischl{at}mgh.harvard.edu)
Support for this research was provided by the National Institutes of Health (NIH), specifically the National Institute of Diabetes and Digestive and Kidney Diseases (K01DK101631), the National Institute on Aging (R56AG068261, R01AG022381, R01AG016495), the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, R01EB019956, R01EB023281), and the National Institute for Neurological Disorders and Stroke (R01NS105820, R01NS083534, U01NS086625). Additional support was provided by the BrightFocus Foundation (A2016172S).
Computational resources were provided by NIH Shared Instrumentation Grants (S10RR023401, S10RR019307, S10RR023043, S10RR028832), the O2 High Performance Compute Cluster at Harvard Medical School, the Enterprise Research Infrastructure & Services at Mass General Brigham (MGB), and the AWS Cloud Credits for Research program.
I. Aganj (iaganj{at}mgh.harvard.edu, +1-617-724-5652) and B. Fischl (bfischl{at}mgh.harvard.edu)
B. Fischl has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. B. Fischl’s interests were reviewed and are managed by MGH and MGB according to their conflict of interest policies.
New experimental results have been added.