812 research outputs found
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
-weighted and -weighted contrasts with only -weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap), with a fast run time~( 45
seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Nonlinear Markov Random Fields Learned via Backpropagation
Although convolutional neural networks (CNNs) currently dominate competitions
on image segmentation, for neuroimaging analysis tasks, more classical
generative approaches based on mixture models are still used in practice to
parcellate brains. To bridge the gap between the two, in this paper we propose
a marriage between a probabilistic generative model, which has been shown to be
robust to variability among magnetic resonance (MR) images acquired via
different imaging protocols, and a CNN. The link is in the prior distribution
over the unknown tissue classes, which are classically modelled using a Markov
random field. In this work we model the interactions among neighbouring pixels
by a type of recurrent CNN, which can encode more complex spatial interactions.
We validate our proposed model on publicly available MR data, from different
centres, and show that it generalises across imaging protocols. This result
demonstrates a successful and principled inclusion of a CNN in a generative
model, which in turn could be adapted by any probabilistic generative approach
for image segmentation.Comment: Accepted for the international conference on Information Processing
in Medical Imaging (IPMI) 2019, camera ready versio
Towards continual learning in medical imaging
This work investigates continual learning of two segmentation tasks in brain MRI with neural networks. To explore in this context the capabilities of current methods for countering catastrophic forgetting of the first task when a new one is learned, we investigate elastic weight consolidation, a recently proposed method based on Fisher information, originally evaluated on reinforcement learning of Atari games. We use it to sequentially learn segmentation of normal brain structures and then segmentation of white matter lesions. Our findings show this recent method reduces catastrophic forgetting, while large room for improvement exists in these challenging settings for continual learning
Multi-modal segmentation of 3D brain scans using neural networks
Purpose: To implement a brain segmentation pipeline based on convolutional
neural networks, which rapidly segments 3D volumes into 27 anatomical
structures. To provide an extensive, comparative study of segmentation
performance on various contrasts of magnetic resonance imaging (MRI) and
computed tomography (CT) scans. Methods: Deep convolutional neural networks are
trained to segment 3D MRI (MPRAGE, DWI, FLAIR) and CT scans. A large database
of in total 851 MRI/CT scans is used for neural network training. Training
labels are obtained on the MPRAGE contrast and coregistered to the other
imaging modalities. The segmentation quality is quantified using the Dice
metric for a total of 27 anatomical structures. Dropout sampling is implemented
to identify corrupted input scans or low-quality segmentations. Full
segmentation of 3D volumes with more than 2 million voxels is obtained in less
than 1s of processing time on a graphical processing unit. Results: The best
average Dice score is found on -weighted MPRAGE ().
However, for FLAIR (), DWI () and CT (), good-quality segmentation is feasible for most anatomical
structures. Corrupted input volumes or low-quality segmentations can be
detected using dropout sampling. Conclusion: The flexibility and performance of
deep convolutional neural networks enables the direct, real-time segmentation
of FLAIR, DWI and CT scans without requiring -weighted scans
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