Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

By M. Jorge Cardoso, Marc Modat, Robin Wolz, A. Melbourne, D. Cash, Daniel Rueckert, Sébastien Ourselin. IEEE Transactions on Medical Imaging, 2015

375 citations recorded by the source corpus.

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/f60db50a13b7f3c8c2aa40248ba64e5d1adbe6ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020, background): “1 shows the superposition of an MRI and a corresponding rain parcellation [12] with the same size ( 181 × 181 ) but different rigin, spacing and orientation.”
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task (2017, methodology): “The bronze standard parcellation of 155 brain structures and 5 nonbrain outer tissues are obtained using the GIF framework [1].”