Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value

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.

Automated summary

A new approach for medical image segmentation is proposed, which calculates the probability of all possible atlas-to-image transformations and the expected label value (ELV), avoiding the issue of local optima. This method does not require actually performing deformable registration, hence saving computational costs.