169 research outputs found
Sharing images: The why and how of medical imaging analysis research
Presentation on Sharing images: The why and how of medical imaging analysis research for the Health-RI infrastructure for the Health-RI FAIR data stewards basics course in Utrecht, on 3 July 202
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge
Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org
Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling
Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming
abnormal, the pathophysiology of which is very complex and largely unknown.
Event-based modeling (EBM) is a data-driven technique to estimate the sequence
in which biomarkers for a disease become abnormal based on cross-sectional
data. It can help in understanding the dynamics of disease progression and
facilitate early diagnosis and prognosis. In this work we propose a novel
discriminative approach to EBM, which is shown to be more accurate than
existing state-of-the-art EBM methods. The method first estimates for each
subject an approximate ordering of events. Subsequently, the central ordering
over all subjects is estimated by fitting a generalized Mallows model to these
approximate subject-specific orderings. We also introduce the concept of
relative distance between events which helps in creating a disease progression
timeline. Subsequently, we propose a method to stage subjects by placing them
on the estimated disease progression timeline. We evaluated the proposed method
on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the
results with existing state-of-the-art EBM methods. We also performed extensive
experiments on synthetic data simulating the progression of Alzheimer's
disease. The event orderings obtained on ADNI data seem plausible and are in
agreement with the current understanding of progression of AD. The proposed
patient staging algorithm performed consistently better than that of
state-of-the-art EBM methods. Event orderings obtained in simulation
experiments were more accurate than those of other EBM methods and the
estimated disease progression timeline was observed to correlate with the
timeline of actual disease progression. The results of these experiments are
encouraging and suggest that discriminative EBM is a promising approach to
disease progression modeling
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis
Confounding bias is a crucial problem when applying machine learning to
practice, especially in clinical practice. We consider the problem of learning
representations independent to multiple biases. In literature, this is mostly
solved by purging the bias information from learned representations. We however
expect this strategy to harm the diversity of information in the
representation, and thus limiting its prospective usage (e.g., interpretation).
Therefore, we propose to mitigate the bias while keeping almost all information
in the latent representations, which enables us to observe and interpret them
as well. To achieve this, we project latent features onto a learned vector
direction, and enforce the independence between biases and projected features
rather than all learned features. To interpret the mapping between projected
features and input data, we propose projection-wise disentangling: a sampling
and reconstruction along the learned vector direction. The proposed method was
evaluated on the analysis of 3D facial shape and patient characteristics
(N=5011). Experiments showed that this conceptually simple method achieved
state-of-the-art fair prediction performance and interpretability, showing its
great potential for clinical applications.Comment: Accepted at MICCAI 202
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
Tract-specific diffusion measures, as derived from brain diffusion MRI, have
been linked to white matter tract structural integrity and neurodegeneration.
As a consequence, there is a large interest in the automatic segmentation of
white matter tract in diffusion tensor MRI data. Methods based on the
tractography are popular for white matter tract segmentation. However, because
of the limited consistency and long processing time, such methods may not be
suitable for clinical practice. We therefore developed a novel convolutional
neural network based method to directly segment white matter tract trained on a
low-resolution dataset of 9149 DTI images. The method is optimized on input,
loss function and network architecture selections. We evaluated both
segmentation accuracy and reproducibility, and reproducibility of determining
tract-specific diffusion measures. The reproducibility of the method is higher
than that of the reference standard and the determined diffusion measures are
consistent. Therefore, we expect our method to be applicable in clinical
practice and in longitudinal analysis of white matter microstructure.Comment: Machine Learning in Medical Imaging (MLMI), 201
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
To accurately analyze changes of anatomical structures in longitudinal
imaging studies, consistent segmentation across multiple time-points is
required. Existing solutions often involve independent registration and
segmentation components. Registration between time-points is used either as a
prior for segmentation in a subsequent time point or to perform segmentation in
a common space. In this work, we propose a novel hybrid convolutional neural
network (CNN) that integrates segmentation and registration into a single
procedure. We hypothesize that the joint optimization leads to increased
performance on both tasks. The hybrid CNN is trained by minimizing an
integrated loss function composed of four different terms, measuring
segmentation accuracy, similarity between registered images, deformation field
smoothness, and segmentation consistency. We applied this method to the
segmentation of white matter tracts, describing functionally grouped axonal
fibers, using N=8045 longitudinal brain MRI data of 3249 individuals. The
proposed method was compared with two multistage pipelines using two existing
segmentation methods combined with a conventional deformable registration
algorithm. In addition, we assessed the added value of the joint optimization
for segmentation and registration separately. The hybrid CNN yielded
significantly higher accuracy, consistency and reproducibility of segmentation
than the multistage pipelines, and was orders of magnitude faster. Therefore,
we expect it can serve as a novel tool to support clinical and epidemiological
analyses on understanding microstructural brain changes over time.Comment: MICCAI 2019 (oral presentation
AI-based association analysis for medical imaging using latent-space geometric confounder correction
AI has greatly enhanced medical image analysis, yet its use in
epidemiological population imaging studies remains limited due to visualization
challenges in non-linear models and lack of confounder control. Addressing
this, we introduce an AI method emphasizing semantic feature interpretation and
resilience against multiple confounders. Our approach's merits are tested in
three scenarios: extracting confounder-free features from a 2D synthetic
dataset; examining the association between prenatal alcohol exposure and
children's facial shapes using 3D mesh data; exploring the relationship between
global cognition and brain images with a 3D MRI dataset. Results confirm our
method effectively reduces confounder influences, establishing less confounded
associations. Additionally, it provides a unique visual representation,
highlighting specific image alterations due to identified correlations.Comment: 18 pages; 7 figure
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
Analysis of longitudinal changes in imaging studies often involves both
segmentation of structures of interest and registration of multiple timeframes.
The accuracy of such analysis could benefit from a tailored framework that
jointly optimizes both tasks to fully exploit the information available in the
longitudinal data. Most learning-based registration algorithms, including joint
optimization approaches, currently suffer from bias due to selection of a fixed
reference frame and only support pairwise transformations. We here propose an
analytical framework based on an unbiased learning strategy for group-wise
registration that simultaneously registers images to the mean space of a group
to obtain consistent segmentations. We evaluate the proposed method on
longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3
time-points for 3249 individuals, i.e., 8045 images in total. The
reproducibility of the method is evaluated on test-retest data from 97
individuals. The results confirm that the implicit reference image is an
average of the input image. In addition, the proposed framework leads to
consistent segmentations and significantly lower processing bias than that of a
pair-wise fixed-reference approach. This processing bias is even smaller than
those obtained when translating segmentations by only one voxel, which can be
attributed to subtle numerical instabilities and interpolation. Therefore, we
postulate that the proposed mean-space learning strategy could be widely
applied to learning-based registration tasks. In addition, this group-wise
framework introduces a novel way for learning-based longitudinal studies by
direct construction of an unbiased within-subject template and allowing
reliable and efficient analysis of spatio-temporal imaging biomarkers.Comment: SPIE Medical Imaging 2021 (oral
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