6 research outputs found
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy
International audiencePlanning of radiotherapy involves accurate segmentation of a large number of organs at risk (OAR), i.e., organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of OAR inside the head, from magnetic resonance images (MRIs). Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem, and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and nonexclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step. In particular, we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered OAR, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs were evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 to 0.7 mm across different organs. A vast majority (96%) of the produced segmentations were found acceptable for radiotherapy planning
Multi-Atlas Segmentation of Biomedical Images: A Survey
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing
Assessing Selection Methods in the Context of Multi-atlas Based Segmentation
International audienceno abstrac