64 research outputs found
Deep learning-based fully automatic segmentation of wrist cartilage in MR images
The study objective was to investigate the performance of a dedicated
convolutional neural network (CNN) optimized for wrist cartilage segmentation
from 2D MR images. CNN utilized a planar architecture and patch-based (PB)
training approach that ensured optimal performance in the presence of a limited
amount of training data. The CNN was trained and validated in twenty
multi-slice MRI datasets acquired with two different coils in eleven subjects
(healthy volunteers and patients). The validation included a comparison with
the alternative state-of-the-art CNN methods for the segmentation of joints
from MR images and the ground-truth manual segmentation. When trained on the
limited training data, the CNN outperformed significantly image-based and
patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with
manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the
representative (central coronal) slices with large amount of cartilage tissue.
Reduced performance of the network for slices with a very limited amount of
cartilage tissue suggests the need for fully 3D convolutional networks to
provide uniform performance across the joint. The study also assessed inter-
and intra-observer variability of the manual wrist cartilage segmentation
(DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based
segmentation of the wrist cartilage from MRI could facilitate research of novel
imaging markers of wrist osteoarthritis to characterize its progression and
response to therapy
Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion
Dynamic MRI may capture temporal anatomical changes in soft tissue organs
with high contrast but the obtained sequences usually suffer from limited
volume coverage which makes the high resolution reconstruction of organ shape
trajectories a major challenge in temporal studies. Because of the variability
of abdominal organ shapes across time and subjects, the objective of this study
is to go towards 3D dense velocity measurements to fully cover the entire
surface and to extract meaningful features characterizing the observed organ
deformations and enabling clinical action or decision. We present a pipeline
for characterization of bladder surface dynamics during deep respiratory
movements. For a compact shape representation, the reconstructed temporal
volumes were first used to establish subject-specific dynamical 4D mesh
sequences using the LDDMM framework. Then, we performed a statistical
characterization of organ dynamics from mechanical parameters such as mesh
elongations and distortions. Since we refer to organs as non flat surfaces, we
have also used the mean curvature changes as metric to quantify surface
evolution. However, the numerical computation of curvature is strongly
dependant on the surface parameterization. To cope with this dependency, we
employed a new method for surface deformation analysis. Independent of
parameterization and minimizing the length of the geodesic curves, it stretches
smoothly the surface curves towards a sphere by minimizing a Dirichlet energy.
An Eulerian PDE approach is used to derive a shape descriptor from the
curve-shortening flow. Intercorrelations between individual motion patterns are
computed using the Laplace Beltrami operator eigenfunctions for spherical
mapping. Application to extracting characterization correlation curves for
locally controlled simulated shape trajectories demonstrates the stability of
the proposed shape descriptor.Comment: arXiv admin note: text overlap with arXiv:2003.0833
PROCEDE ET DISPOSITIF DE SEGMENTATION D'IMAGES PAR PROPAGATION AUTOMATIQUE DANS UNE (N+1)-IEME DIMENSION D'UNE SEGMENTATION D'IMAGES INITIALISEE EN DIMENSION N
PROCEDE ET DISPOSITIF DE SEGMENTATION D'IMAGES PAR PROPAGATION AUTOMATIQUE DANS UNE (N+1)-IEME DIMENSION D'UNE SEGMENTATION D'IMAGES INITIALISEE EN DIMENSION N
Four-dimensional reconstruction and characterization of bladder deformations
International audienc
Three-dimensional reconstruction and characterization of bladder deformations
International audienc
Whole-muscle fat analysis identifies distal muscle end as disease initiation site in facioscapulohumeral muscular dystrophy
International audienceAbstract Background Facioscapulohumeral dystrophy (FSHD) is a major muscular dystrophy characterized by asymmetric fatty replacement of muscles. We aimed to determine the initiation site and progression profile of the disease in lower extremity muscles of FSHD patients by assessing fat infiltration along their full proximo-distal axis using quantitative MRI. Methods Nine patients underwent MRI of lower extremities to assess end-to-end muscle fat fractions (FFs) and inflammatory lesions. Seven patients underwent the same MRI ~3.5 years later. Individual muscles (n = 396) were semi-automatically segmented to calculate average FFs over all slices covering whole muscles. To assess disease progression we determined FF changes in 5 adjacent muscle segments. Results We provide evidence that fat replacement commonly starts at the distal end of affected muscles where the highest FFs occur (p < 0.001). It progresses in a wave-like manner in the proximal direction at an increasing rate with the highest value (4.9 ± 2.7%/year) for muscles with baseline FFs of 30–40%. Thereafter it proceeds at a slower pace towards the proximal muscle end. In early phases of disease, inflammatory lesions preferentially occur at the distal muscle end. Compared with whole-muscle analysis, the common FF assessments using only few MR slices centrally placed in muscles are significantly biased (~50% in progression rate). Conclusions These findings identify the distal end of leg muscles as a prime location for disease initiation in FSHD and demonstrate a wave-like progression towards the proximal end, consistent with proposed disease mechanisms. End-to-end whole-muscle fat assessment is essential to properly diagnose FSHD and its progression
Muscular fat infiltration in FSHD starts with a “fat burst” near the distal tendon and advances towards the proximal tendon
International audienc
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