PT  - JOURNAL ARTICLE
AU  - Iman Aganj
AU  - Bruce Fischl
TI  - Multi-Atlas Image Soft-Segmentation via Computation of the Expected Label Value
AID  - 10.1101/2020.10.08.331553
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.08.331553
4099  - http://biorxiv.org/content/early/2020/10/16/2020.10.08.331553.short
4100  - http://biorxiv.org/content/early/2020/10/16/2020.10.08.331553.full
AB  - 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 StatementB. 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.