72 research outputs found
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We present a method for fast biomedical image atlas construction using neural
fields. Atlases are key to biomedical image analysis tasks, yet conventional
and deep network estimation methods remain time-intensive. In this preliminary
work, we frame subject-specific atlas building as learning a neural field of
deformable spatiotemporal observations. We apply our method to learning
subject-specific atlases and motion stabilization of dynamic BOLD MRI
time-series of fetuses in utero. Our method yields high-quality atlases of
fetal BOLD time-series with 5-7 faster convergence compared to
existing work. While our method slightly underperforms well-tuned baselines in
terms of anatomical overlap, it estimates templates significantly faster, thus
enabling rapid processing and stabilization of large databases of 4D dynamic
MRI acquisitions. Code is available at
https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202
Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction
We present Hybrid-CSR, a geometric deep-learning model that combines explicit
and implicit shape representations for cortical surface reconstruction.
Specifically, Hybrid-CSR begins with explicit deformations of template meshes
to obtain coarsely reconstructed cortical surfaces, based on which the oriented
point clouds are estimated for the subsequent differentiable poisson surface
reconstruction. By doing so, our method unifies explicit (oriented point
clouds) and implicit (indicator function) cortical surface reconstruction.
Compared to explicit representation-based methods, our hybrid approach is more
friendly to capture detailed structures, and when compared with implicit
representation-based methods, our method can be topology aware because of
end-to-end training with a mesh-based deformation module. In order to address
topology defects, we propose a new topology correction pipeline that relies on
optimization-based diffeomorphic surface registration. Experimental results on
three brain datasets show that our approach surpasses existing implicit and
explicit cortical surface reconstruction methods in numeric metrics in terms of
accuracy, regularity, and consistency
Asclepios: a Research Project-Team at INRIA for the Analysis and Simulation of Biomedical Images
International audienceAsclepios1 is the name of a research project-team o cially launched on November 1st, 2005 at INRIA Sophia-Antipolis, to study the Analysis and Simulation of Biological and Medical Images. This research project-team follows a previous one, called Epidaure, initially dedicated to Medical Imaging and Robotics research. These two project teams were strongly supported by Gilles Kahn, who used to have regular scienti c in- teractions with their members. More generally, Gilles Kahn had a unique vision of the growing importance of the interaction of the Information Technologies and Sciences with the Biological and Medical world. He was one of the originators of the creation of a speci c BIO theme among the main INRIA research directions, which now regroups 16 di fferent research teams including Asclepios, whose research objectives are described and illustrated in this article
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
U-Net style networks are commonly utilized in unsupervised image registration
to predict dense displacement fields, which for high-resolution volumetric
image data is a resource-intensive and time-consuming task. To tackle this
challenge, we first propose Fourier-Net, which replaces the costly U-Net style
expansive path with a parameter-free model-driven decoder. Instead of directly
predicting a full-resolution displacement field, our Fourier-Net learns a
low-dimensional representation of the displacement field in the band-limited
Fourier domain which our model-driven decoder converts to a full-resolution
displacement field in the spatial domain. Expanding upon Fourier-Net, we then
introduce Fourier-Net+, which additionally takes the band-limited spatial
representation of the images as input and further reduces the number of
convolutional layers in the U-Net style network's contracting path. Finally, to
enhance the registration performance, we propose a cascaded version of
Fourier-Net+. We evaluate our proposed methods on three datasets, on which our
proposed Fourier-Net and its variants achieve comparable results with current
state-of-the art methods, while exhibiting faster inference speeds, lower
memory footprint, and fewer multiply-add operations. With such small
computational cost, our Fourier-Net+ enables the efficient training of
large-scale 3D registration on low-VRAM GPUs. Our code is publicly available at
\url{https://github.com/xi-jia/Fourier-Net}.Comment: Under review. arXiv admin note: text overlap with arXiv:2211.1634
Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.
The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed
Ultrasound-Augmented Laparoscopy
Laparoscopic surgery is perhaps the most common minimally invasive procedure for many diseases in the abdomen. Since the laparoscopic camera provides only the surface view of the internal organs, in many procedures, surgeons use laparoscopic ultrasound (LUS) to visualize deep-seated surgical targets. Conventionally, the 2D LUS image is visualized in a display spatially separate from that displays the laparoscopic video. Therefore, reasoning about the geometry of hidden targets requires mentally solving the spatial alignment, and resolving the modality differences, which is cognitively very challenging. Moreover, the mental representation of hidden targets in space acquired through such cognitive medication may be error prone, and cause incorrect actions to be performed.
To remedy this, advanced visualization strategies are required where the US information is visualized in the context of the laparoscopic video. To this end, efficient computational methods are required to accurately align the US image coordinate system with that centred in the camera, and to render the registered image information in the context of the camera such that surgeons perceive the geometry of hidden targets accurately. In this thesis, such a visualization pipeline is described. A novel method to register US images with a camera centric coordinate system is detailed with an experimental investigation into its accuracy bounds. An improved method to blend US information with the surface view is also presented with an experimental investigation into the accuracy of perception of the target locations in space
SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Tools
Instance segmentation of surgical instruments is a long-standing research
problem, crucial for the development of many applications for computer-assisted
surgery. This problem is commonly tackled via fully-supervised training of deep
learning models, requiring expensive pixel-level annotations to train. In this
work, we develop a framework for instance segmentation not relying on spatial
annotations for training. Instead, our solution only requires binary tool
masks, obtainable using recent unsupervised approaches, and binary tool
presence labels, freely obtainable in robot-assisted surgery. Based on the
binary mask information, our solution learns to extract individual tool
instances from single frames, and to encode each instance into a compact vector
representation, capturing its semantic features. Such representations guide the
automatic selection of a tiny number of instances (8 only in our experiments),
displayed to a human operator for tool-type labelling. The gathered information
is finally used to match each training instance with a binary tool presence
label, providing an effective supervision signal to train a tool instance
classifier. We validate our framework on the EndoVis 2017 and 2018 segmentation
datasets. We provide results using binary masks obtained either by manual
annotation or as predictions of an unsupervised binary segmentation model. The
latter solution yields an instance segmentation approach completely free from
spatial annotations, outperforming several state-of-the-art fully-supervised
segmentation approaches
A non-invasive diagnostic system for early assessment of acute renal transplant rejection.
Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool
USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR
We present USLR, a computational framework for longitudinal registration of
brain MRI scans to estimate nonlinear image trajectories that are smooth across
time, unbiased to any timepoint, and robust to imaging artefacts. It operates
on the Lie algebra parameterisation of spatial transforms (which is compatible
with rigid transforms and stationary velocity fields for nonlinear deformation)
and takes advantage of log-domain properties to solve the problem using
Bayesian inference. USRL estimates rigid and nonlinear registrations that: (i)
bring all timepoints to an unbiased subject-specific space; and (i) compute a
smooth trajectory across the imaging time-series. We capitalise on
learning-based registration algorithms and closed-form expressions for fast
inference. A use-case Alzheimer's disease study is used to showcase the
benefits of the pipeline in multiple fronts, such as time-consistent image
segmentation to reduce intra-subject variability, subject-specific prediction
or population analysis using tensor-based morphometry. We demonstrate that such
approach improves upon cross-sectional methods in identifying group
differences, which can be helpful in detecting more subtle atrophy levels or in
reducing sample sizes in clinical trials. The code is publicly available in
https://github.com/acasamitjana/uslrComment: Submitted to Medical Image Analysi
Optimization and Data Analysis in Biomedical Informatics
Abstract Intravascular ultrasound (IVUS) is a catheter-based medical imaging modality that is capable of providing cross-sectional images of the interior of blood vessels. A comprehensive analysis of the IVUS data allows collecting information about the morphology and structure of the vessel and the atherosclerotic plaque, if present. Atherosclerotic plaque formation is considered to be a part of an inflammatory process. Recent evidence has suggested that the presence and proliferation of vasa vasorum (VV) in the plaque is correlated with the increase of plaque inflammation and the processes which lead to its destabilization. Hence, the detection and measurement of VV in plaque has the potential to enable the development of an index of plaque vulnerability. In this paper, we review the research at the Computational Biomedicine Lab towards the development of a complete pipeline for the detection and quantification of extra-luminal blood detection from IVUS data which may be an indication of the existence of VV
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