1,602 research outputs found
Globally Optimal Coupled Surfaces for Semi-automatic Segmentation of Medical Images
Manual delineations are of paramount importance in medical imaging, for instance to train supervised methods and evaluate automatic segmentation algorithms. In volumetric images, manually tracing regions of interest is an excruciating process in which much time is wasted labeling neighboring 2D slices that are similar to each other. Here we present a method to compute a set of discrete minimal surfaces whose boundaries are specified by user-provided segmentations on one or more planes. Using this method, the user can for example manually delineate one slice every n and let the algorithm complete the segmentation for the slices in between. Using a discrete framework, this method globally minimizes a cost function that combines a regularizer with a data term based on image intensities, while ensuring that the surfaces do not intersect each other or leave holes in between. While the resulting optimization problem is an integer program and thus NP-hard, we show that the equality constraint matrix is totally unimodular, which enables us to solve the linear program (LP) relaxation instead. We can then capitalize on the existence of efficient LP solvers to compute a globally optimal solution in practical times. Experiments on two different datasets illustrate the superiority of the proposed method over the use of independent, label-wise optimal surfaces (∼ 5% mean increase in Dice when one every six slices is labeled, with some structures improving up to ∼ 10% in Dice)
Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images
Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has
been widely used for synthesis of multi-domain medical images. The
domain-specific nonlinear deformations captured by CycleGAN make the
synthesized images difficult to be used for some applications, for example,
generating pseudo-CT for PET-MR attenuation correction. This paper presents a
deformation-invariant CycleGAN (DicycleGAN) method using deformable
convolutional layers and new cycle-consistency losses. Its robustness dealing
with data that suffer from domain-specific nonlinear deformations has been
evaluated through comparison experiments performed on a multi-sequence brain MR
dataset and a multi-modality abdominal dataset. Our method has displayed its
ability to generate synthesized data that is aligned with the source while
maintaining a proper quality of signal compared to CycleGAN-generated data. The
proposed model also obtained comparable performance with CycleGAN when data
from the source and target domains are alignable through simple affine
transformations
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and
cosegmentation with two key characteristics: first, we substitute ground truth
data with the semantic map output of a classifier; second, we combine this
output with population deformable registration to improve both alignment and
segmentation. Our approach deforms all volumes towards consensus, taking into
account image similarities and label consistency. Our pipeline can incorporate
any classifier and similarity metric. Results on two datasets, containing
annotations of challenging brain structures, demonstrate the potential of our
method.Comment: The first two authors contributed equall
Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms
Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variability in the chosen annotated locations and in the interpretation of visual ambiguities. In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration. Our framework: (i) iteratively suggests the most informative location to annotate next, taking into account its redundancy with previous annotations; (ii) extends traditional pointwise annotations by accounting for the spatial uncertainty of each annotation, which can either be directly specified by the user, or aggregated from pointwise annotations from multiple experts; and (iii) naturally provides a new strategy for the evaluation of deformable registration algorithms. Our approach is validated on four different registration tasks. The experimental results show the efficacy of suggesting annotations according to their informativeness, and an improved capacity to assess the quality of the outputs of registration algorithms. In addition, our approach yields, from sparse annotations only, a dense visualization of the errors made by a registration method. The source code of our approach supporting both 2D and 3D data is publicly available at https://github.com/LoicPeter/evaluation-deformable-registration
Thalamic nuclei in frontotemporal dementia: Mediodorsal nucleus involvement is universal but pulvinar atrophy is unique to C9orf72
Thalamic atrophy is a common feature across all forms of FTD but little is known about specific nuclei involvement. We aimed to investigate in vivo atrophy of the thalamic nuclei across the FTD spectrum. A cohort of 402 FTD patients (age: mean(SD) 64.3(8.2) years; disease duration: 4.8(2.8) years) was compared with 104 age‐matched controls (age: 62.5(10.4) years), using an automated segmentation of T1‐weighted MRIs to extract volumes of 14 thalamic nuclei. Stratification was performed by clinical diagnosis (180 behavioural variant FTD (bvFTD), 85 semantic variant primary progressive aphasia (svPPA), 114 nonfluent variant PPA (nfvPPA), 15 PPA not otherwise specified (PPA‐NOS), and 8 with associated motor neurone disease (FTD‐MND), genetic diagnosis (27 MAPT, 28 C9orf72, 18 GRN), and pathological confirmation (37 tauopathy, 38 TDP‐43opathy, 4 FUSopathy). The mediodorsal nucleus (MD) was the only nucleus affected in all FTD subgroups (16–33% smaller than controls). The laterodorsal nucleus was also particularly affected in genetic cases (28–38%), TDP‐43 type A (47%), tau‐CBD (44%), and FTD‐MND (53%). The pulvinar was affected only in the C9orf72 group (16%). Both the lateral and medial geniculate nuclei were also affected in the genetic cases (10–20%), particularly the LGN in C9orf72 expansion carriers. Use of individual thalamic nuclei volumes provided higher accuracy in discriminating between FTD groups than the whole thalamic volume. The MD is the only structure affected across all FTD groups. Differential involvement of the thalamic nuclei among FTD forms is seen, with a unique pattern of atrophy in the pulvinar in C9orf72 expansion carriers
Hierarchical Joint Registration of Tissue Blocks With Soft Shape Constraints For Large-Scale Histology of The Human Brain
Large-scale 3D histology reconstruction of the human
brain with MRI as volumetric reference generally requires
reassembling the tissue blocks into the MRI space, prior to
any further reconstruction of the histology of the individual
blocks. This is a challenging registration problem, particularly in the frequent case that blockface photographs of
paraffin embedded tissue are used as intermediate modality, as their contrast between white and gray matter is rather
low. Here we propose a registration framework to address
this problem, relying on two key components. First, blocks
are simultaneously aligned to the MRI while exploiting the
spatial constraints that they impose on each other, by means
of a customized soft shape constraint (similarly to a jigsaw
puzzle). And second, we adopt a hierarchical optimization
strategy that capitalizes on our prior knowledge on the slicing
and blocking procedure. Our framework is validated quantitatively on synthetic data, and qualitatively on the histology
of a whole human hemisphere
Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning
Optical coherence tomography (OCT) is commonly used to analyze retinal layers
for assessment of ocular diseases. In this paper, we propose a method for
retinal layer segmentation and quantification of uncertainty based on Bayesian
deep learning. Our method not only performs end-to-end segmentation of retinal
layers, but also gives the pixel wise uncertainty measure of the segmentation
output. The generated uncertainty map can be used to identify erroneously
segmented image regions which is useful in downstream analysis. We have
validated our method on a dataset of 1487 images obtained from 15 subjects (OCT
volumes) and compared it against the state-of-the-art segmentation algorithms
that does not take uncertainty into account. The proposed uncertainty based
segmentation method results in comparable or improved performance, and most
importantly is more robust against noise
Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
We propose an unsupervised deep learning method for atlas based registration
to achieve segmentation and spatial alignment of the embryonic brain in a
single framework. Our approach consists of two sequential networks with a
specifically designed loss function to address the challenges in 3D first
trimester ultrasound. The first part learns the affine transformation and the
second part learns the voxelwise nonrigid deformation between the target image
and the atlas. We trained this network end-to-end and validated it against a
ground truth on synthetic datasets designed to resemble the challenges present
in 3D first trimester ultrasound. The method was tested on a dataset of human
embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed
alignment of the brain in some cases and gave insight in open challenges for
the proposed method. We conclude that our method is a promising approach
towards fully automated spatial alignment and segmentation of embryonic brains
in 3D ultrasound
Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy
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