294 research outputs found
Part-to-whole Registration of Histology and MRI using Shape Elements
Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain
magnetic resonance images (MRIs) require acquisition of multiple sequences such
as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid
attenuated inversion recovery (FLAIR) sequence --where lesions appear
hyperintense--. However, most of the existing retrospective datasets do not
consist of FLAIR sequences. Existing missing modality imputation methods
separate the process of imputation, and the process of segmentation. In this
paper, we propose a method to link both modality imputation and segmentation
using convolutional neural networks. We show that by jointly optimizing the
imputation network and the segmentation network, the method not only produces
more realistic synthetic FLAIR images from T1-w images, but also improves the
segmentation of WMH from T1-w images only.Comment: Conference on Medical Imaging with Deep Learning MIDL 201
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challenging
task in machine learning for healthcare that can provide clinicians with better
tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect
temporal dependencies among measurements and make parametric assumptions about
biomarker trajectories. In addition, they do not model multiple biomarkers
jointly and need to align subjects' trajectories. In this paper, recurrent
neural networks (RNNs) are utilized to address these issues. However, in many
cases, longitudinal cohorts contain incomplete data, which hinders the
application of standard RNNs and requires a pre-processing step such as
imputation of the missing values. We, therefore, propose a generalized training
rule for the most widely used RNN architecture, long short-term memory (LSTM)
networks, that can handle missing values in both target and predictor
variables. This algorithm is applied for modeling the progression of
Alzheimer's disease (AD) using magnetic resonance imaging (MRI) biomarkers. The
results show that the proposed LSTM algorithm achieves a lower mean absolute
error for prediction of measurements across all considered MRI biomarkers
compared to using standard LSTM networks with data imputation or using a
regression-based DPM method. Moreover, applying linear discriminant analysis to
the biomarkers' values predicted by the proposed algorithm results in a larger
area under the receiver operating characteristic curve (AUC) for clinical
diagnosis of AD compared to the same alternatives, and the AUC is comparable to
state-of-the-art AUCs from a recent cross-sectional medical image
classification challenge. This paper shows that built-in handling of missing
values in LSTM network training paves the way for application of RNNs in
disease progression modeling.Comment: 9 pages, 1 figure, MIDL conferenc
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challenging
machine learning task. Existing DPM algorithms neglect temporal dependencies
among measurements, make parametric assumptions about biomarker trajectories,
do not model multiple biomarkers jointly, and need an alignment of subjects'
trajectories. In this paper, recurrent neural networks (RNNs) are utilized to
address these issues. However, in many cases, longitudinal cohorts contain
incomplete data, which hinders the application of standard RNNs and requires a
pre-processing step such as imputation of the missing values. Instead, we
propose a generalized training rule for the most widely used RNN architecture,
long short-term memory (LSTM) networks, that can handle both missing predictor
and target values. The proposed LSTM algorithm is applied to model the
progression of Alzheimer's disease (AD) using six volumetric magnetic resonance
imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole
brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is
compared to standard LSTM networks with data imputation and a parametric,
regression-based DPM method. The results show that the proposed algorithm
achieves a significantly lower mean absolute error (MAE) than the alternatives
with p < 0.05 using Wilcoxon signed rank test in predicting values of almost
all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA)
classifier applied to the predicted biomarker values produces a significantly
larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for
clinical diagnosis of AD. Inspection of MAE curves as a function of the amount
of missing data reveals that the proposed LSTM algorithm achieves the best
performance up until more than 74% missing values. Finally, it is illustrated
how the method can successfully be applied to data with varying time intervals.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0550
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Medical image processing tasks such as segmentation often require capturing
non-local information. As organs, bones, and tissues share common
characteristics such as intensity, shape, and texture, the contextual
information plays a critical role in correctly labeling them. Segmentation and
labeling is now typically done with convolutional neural networks (CNNs) but
the context of the CNN is limited by the receptive field which itself is
limited by memory requirements and other properties. In this paper, we propose
a new attention module, that we call Permutohedral Attention Module (PAM), to
efficiently capture non-local characteristics of the image. The proposed method
is both memory and computationally efficient. We provide a GPU implementation
of this module suitable for 3D medical imaging problems. We demonstrate the
efficiency and scalability of our module with the challenging task of vertebrae
segmentation and labeling where context plays a crucial role because of the
very similar appearance of different vertebrae.Comment: Accepted at MICCAI-201
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission
tomography (PET) image reconstruction to allow for accurate quantification.
However, attenuation correction is particularly challenging for PET-MRI as
neither PET nor magnetic resonance imaging (MRI) can directly image tissue
attenuation properties. MRI-based computed tomography (CT) synthesis has been
proposed as an alternative to physics based and segmentation-based approaches
that assign a population-based tissue density value in order to generate an
attenuation map. We propose a novel deep fully convolutional neural network
that generates synthetic CTs in a recursive manner by gradually reducing the
residuals of the previous network, increasing the overall accuracy and
generalisability, while keeping the number of trainable parameters within
reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
pairs and a four-fold random bootstrapped validation with a 80:20 split is
performed. Quantitative results show that the proposed framework outperforms a
state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201
- …