53 research outputs found
Deep Neural Networks for Anatomical Brain Segmentation
We present a novel approach to automatically segment magnetic resonance (MR)
images of the human brain into anatomical regions. Our methodology is based on
a deep artificial neural network that assigns each voxel in an MR image of the
brain to its corresponding anatomical region. The inputs of the network capture
information at different scales around the voxel of interest: 3D and orthogonal
2D intensity patches capture the local spatial context while large, compressed
2D orthogonal patches and distances to the regional centroids enforce global
spatial consistency. Contrary to commonly used segmentation methods, our
technique does not require any non-linear registration of the MR images. To
benchmark our model, we used the dataset provided for the MICCAI 2012 challenge
on multi-atlas labelling, which consists of 35 manually segmented MR images of
the brain. We obtained competitive results (mean dice coefficient 0.725, error
rate 0.163) showing the potential of our approach. To our knowledge, our
technique is the first to tackle the anatomical segmentation of the whole brain
using deep neural networks
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Microscopic analysis of histological sections is considered the "gold
standard" to verify structural parcellations in the human brain. Its high
resolution allows the study of laminar and columnar patterns of cell
distributions, which build an important basis for the simulation of cortical
areas and networks. However, such cytoarchitectonic mapping is a semiautomatic,
time consuming process that does not scale with high throughput imaging. We
present an automatic approach for parcellating histological sections at 2um
resolution. It is based on a convolutional neural network that combines
topological information from probabilistic atlases with the texture features
learned from high-resolution cell-body stained images. The model is applied to
visual areas and trained on a sparse set of partial annotations. We show how
predictions are transferable to new brains and spatially consistent across
sections.Comment: Accepted for oral presentation at International Symposium of
Biomedical Imaging (ISBI) 201
To Learn or Not to Learn Features for Deformable Registration?
Feature-based registration has been popular with a variety of features
ranging from voxel intensity to Self-Similarity Context (SSC). In this paper,
we examine the question on how features learnt using various Deep Learning (DL)
frameworks can be used for deformable registration and whether this feature
learning is necessary or not. We investigate the use of features learned by
different DL methods in the current state-of-the-art discrete registration
framework and analyze its performance on 2 publicly available datasets. We draw
insights into the type of DL framework useful for feature learning and the
impact, if any, of the complexity of different DL models and brain parcellation
methods on the performance of discrete registration. Our results indicate that
the registration performance with DL features and SSC are comparable and stable
across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
Whole brain segmentation with magnetic resonance imaging (MRI) enables the
non-invasive measurement of brain regions, including total intracranial volume
(TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain
segmentation methodology to incorporate intracranial measurements offers a
heightened level of comprehensiveness in the analysis of brain structures.
Despite its potential, the task of generalizing deep learning techniques for
intracranial measurements faces data availability constraints due to limited
manually annotated atlases encompassing whole brain and TICV/PFV labels. In
this paper, we enhancing the hierarchical transformer UNesT for whole brain
segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV
simultaneously. To address the problem of data scarcity, the model is first
pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites.
These volumes are processed through a multi-atlas segmentation pipeline for
label generation, while TICV/PFV labels are unavailable. Subsequently, the
model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging
Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are
available. We evaluate our method with Dice similarity coefficients(DSC). We
show that our model is able to conduct precise TICV/PFV estimation while
maintaining the 132 brain regions performance at a comparable level. Code and
trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg
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