25 research outputs found
Multimodality Biomedical Image Registration using Free Point Transformer Networks
We describe a point-set registration algorithm based on a novel free point
transformer (FPT) network, designed for points extracted from multimodal
biomedical images for registration tasks, such as those frequently encountered
in ultrasound-guided interventional procedures. FPT is constructed with a
global feature extractor which accepts unordered source and target point-sets
of variable size. The extracted features are conditioned by a shared multilayer
perceptron point transformer module to predict a displacement vector for each
source point, transforming it into the target space. The point transformer
module assumes no vicinity or smoothness in predicting spatial transformation
and, together with the global feature extractor, is trained in a data-driven
fashion with an unsupervised loss function. In a multimodal registration task
using prostate MR and sparsely acquired ultrasound images, FPT yields
comparable or improved results over other rigid and non-rigid registration
methods. This demonstrates the versatility of FPT to learn registration
directly from real, clinical training data and to generalize to a challenging
task, such as the interventional application presented.Comment: 10 pages, 4 figures. Accepted for publication at International
Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI) workshop on Advances in Simplifying Medical UltraSound (ASMUS) 202
Meta-Learning Initializations for Interactive Medical Image Registration
We present a meta-learning framework for interactive medical image
registration. Our proposed framework comprises three components: a
learning-based medical image registration algorithm, a form of user interaction
that refines registration at inference, and a meta-learning protocol that
learns a rapidly adaptable network initialization. This paper describes a
specific algorithm that implements the registration, interaction and
meta-learning protocol for our exemplar clinical application: registration of
magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled
transrectal ultrasound (TRUS) images. Our approach obtains comparable
registration error (4.26 mm) to the best-performing non-interactive
learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the
data, and occurring in real-time during acquisition. Applying sparsely sampled
data to non-interactive methods yields higher registration errors (6.26 mm),
demonstrating the effectiveness of interactive MR-TRUS registration, which may
be applied intraoperatively given the real-time nature of the adaptation
process.Comment: 11 pages, 10 figures. Paper accepted to IEEE Transactions on Medical
Imaging (October 26 2022
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
We propose Boundary-RL, a novel weakly supervised segmentation method that
utilises only patch-level labels for training. We envision the segmentation as
a boundary detection problem, rather than a pixel-level classification as in
previous works. This outlook on segmentation may allow for boundary delineation
under challenging scenarios such as where noise artefacts may be present within
the region-of-interest (ROI) boundaries, where traditional pixel-level
classification-based weakly supervised methods may not be able to effectively
segment the ROI. Particularly of interest, ultrasound images, where intensity
values represent acoustic impedance differences between boundaries, may also
benefit from the boundary delineation approach. Our method uses reinforcement
learning to train a controller function to localise boundaries of ROIs using a
reward derived from a pre-trained boundary-presence classifier. The classifier
indicates when an object boundary is encountered within a patch, as the
controller modifies the patch location in a sequential Markov decision process.
The classifier itself is trained using only binary patch-level labels of object
presence, which are the only labels used during training of the entire boundary
delineation framework, and serves as a weak signal to inform the boundary
delineation. The use of a controller function ensures that a sliding window
over the entire image is not necessary. It also prevents possible
false-positive or -negative cases by minimising number of patches passed to the
boundary-presence classifier. We evaluate our proposed approach for a
clinically relevant task of prostate gland segmentation on trans-rectal
ultrasound images. We show improved performance compared to other tested weakly
supervised methods, using the same labels e.g., multiple instance learning.Comment: Accepted to MICCAI Workshop MLMI 2023 (14th International Conference
on Machine Learning in Medical Imaging
Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation
Image quality assessment (IQA) in medical imaging can be used to ensure that
downstream clinical tasks can be reliably performed. Quantifying the impact of
an image on the specific target tasks, also named as task amenability, is
needed. A task-specific IQA has recently been proposed to learn an
image-amenability-predicting controller simultaneously with a target task
predictor. This allows for the trained IQA controller to measure the impact an
image has on the target task performance, when this task is performed using the
predictor, e.g. segmentation and classification neural networks in modern
clinical applications. In this work, we propose an extension to this
task-specific IQA approach, by adding a task-agnostic IQA based on
auto-encoding as the target task. Analysing the intersection between
low-quality images, deemed by both the task-specific and task-agnostic IQA, may
help to differentiate the underpinning factors that caused the poor target task
performance. For example, common imaging artefacts may not adversely affect the
target task, which would lead to a low task-agnostic quality and a high
task-specific quality, whilst individual cases considered clinically
challenging, which can not be improved by better imaging equipment or
protocols, is likely to result in a high task-agnostic quality but a low
task-specific quality. We first describe a flexible reward shaping strategy
which allows for the adjustment of weighting between task-agnostic and
task-specific quality scoring. Furthermore, we evaluate the proposed algorithm
using a clinically challenging target task of prostate tumour segmentation on
multiparametric magnetic resonance (mpMR) images, from 850 patients. The
proposed reward shaping strategy, with appropriately weighted task-specific and
task-agnostic qualities, successfully identified samples that need
re-acquisition due to defected imaging process.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://www.melba-journal.or
DeepReg: a deep learning toolkit for medical image registration
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported
open-source toolkit for research and education in medical image registration
using deep learning.Comment: Accepted in The Journal of Open Source Software (JOSS
The James Webb Space Telescope Mission
Twenty-six years ago a small committee report, building on earlier studies,
expounded a compelling and poetic vision for the future of astronomy, calling
for an infrared-optimized space telescope with an aperture of at least .
With the support of their governments in the US, Europe, and Canada, 20,000
people realized that vision as the James Webb Space Telescope. A
generation of astronomers will celebrate their accomplishments for the life of
the mission, potentially as long as 20 years, and beyond. This report and the
scientific discoveries that follow are extended thank-you notes to the 20,000
team members. The telescope is working perfectly, with much better image
quality than expected. In this and accompanying papers, we give a brief
history, describe the observatory, outline its objectives and current observing
program, and discuss the inventions and people who made it possible. We cite
detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space
Telescope Overview, 29 pages, 4 figure
Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial
SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication