906 research outputs found
Function-based Intersubject Alignment of Human Cortical Anatomy
Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
We consider the problem of segmenting a biomedical image into anatomical
regions of interest. We specifically address the frequent scenario where we
have no paired training data that contains images and their manual
segmentations. Instead, we employ unpaired segmentation images to build an
anatomical prior. Critically these segmentations can be derived from imaging
data from a different dataset and imaging modality than the current task. We
introduce a generative probabilistic model that employs the learned prior
through a convolutional neural network to compute segmentations in an
unsupervised setting. We conducted an empirical analysis of the proposed
approach in the context of structural brain MRI segmentation, using a
multi-study dataset of more than 14,000 scans. Our results show that an
anatomical prior can enable fast unsupervised segmentation which is typically
not possible using standard convolutional networks. The integration of
anatomical priors can facilitate CNN-based anatomical segmentation in a range
of novel clinical problems, where few or no annotations are available and thus
standard networks are not trainable. The code is freely available at
http://github.com/adalca/neuron.Comment: Presented at CVPR 2018. IEEE CVPR proceedings pp. 9290-929
An Unsupervised Learning Model for Deformable Medical Image Registration
We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201
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