8 research outputs found
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration
Neural networks have been proposed for medical image registration by
learning, with a substantial amount of training data, the optimal
transformations between image pairs. These trained networks can further be
optimized on a single pair of test images - known as test-time optimization.
This work formulates image registration as a meta-learning algorithm. Such
networks can be trained by aligning the training image pairs while
simultaneously improving test-time optimization efficacy; tasks which were
previously considered two independent training and optimization processes. The
proposed meta-registration is hypothesized to maximize the efficiency and
effectiveness of the test-time optimization in the "outer" meta-optimization of
the networks. For image guidance applications that often are time-critical yet
limited in training data, the potentially gained speed and accuracy are
compared with classical registration algorithms, registration networks without
meta-learning, and single-pair optimization without test-time optimization
data. Experiments are presented in this paper using clinical transrectal
ultrasound image data from 108 prostate cancer patients. These experiments
demonstrate the effectiveness of a meta-registration protocol, which yields
significantly improved performance relative to existing learning-based methods.
Furthermore, the meta-registration achieves comparable results to classical
iterative methods in a fraction of the time, owing to its rapid test-time
optimization process.Comment: Accepted to ASMUS 2022 Workshop at MICCA
Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data
Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid
point set registration approach using deep neural networks. As FPT does not
assume constraints based on point vicinity or correspondence, it may be trained
simply and in a flexible manner by minimizing an unsupervised loss based on the
Chamfer Distance. This makes FPT amenable to real-world medical imaging
applications where ground-truth deformations may be infeasible to obtain, or in
scenarios where only a varying degree of completeness in the point sets to be
aligned is available. To test the limit of the correspondence finding ability
of FPT and its dependency on training data sets, this work explores the
generalizability of the FPT from well-curated non-medical data sets to medical
imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate
its effectiveness and the superior registration performance of FPT over
iterative and learning-based point set registration methods. Second, we
demonstrate superior performance in rigid and non-rigid registration and
robustness to missing data. Last, we highlight the interesting generalizability
of the ModelNet-trained FPT by registering reconstructed freehand ultrasound
scans of the spine and generic spine models without additional training,
whereby the average difference to the ground truth curvatures is 1.3 degrees,
across 13 patients.Comment: Accepted to ASMUS 2022 Workshop at MICCA
Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction
Artificial intelligence-based analysis of lung ultrasound imaging has been
demonstrated as an effective technique for rapid diagnostic decision support
throughout the COVID-19 pandemic. However, such techniques can require days- or
weeks-long training processes and hyper-parameter tuning to develop intelligent
deep learning image analysis models. This work focuses on leveraging
'off-the-shelf' pre-trained models as deep feature extractors for scoring
disease severity with minimal training time. We propose using pre-trained
initializations of existing methods ahead of simple and compact neural networks
to reduce reliance on computational capacity. This reduction of computational
capacity is of critical importance in time-limited or resource-constrained
circumstances, such as the early stages of a pandemic. On a dataset of 49
patients, comprising over 20,000 images, we demonstrate that the use of
existing methods as feature extractors results in the effective classification
of COVID-19-related pneumonia severity while requiring only minutes of training
time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity
score scale and provides comparable per-patient region and global scores
compared to expert annotated ground truths. These results demonstrate the
capability for rapid deployment and use of such minimally-adapted methods for
progress monitoring, patient stratification and management in clinical practice
for COVID-19 patients, and potentially in other respiratory diseases.Comment: Accepted to ASMUS 2022 Workshop at MICCA
Image quality assessment for machine learning tasks using meta-reinforcement learning
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images
Non-rigid Medical Image Registration using Physics-informed Neural Networks
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for modelling prostate motion commonly encountered during transrectal ultrasound guided procedures. To overcome a widely-recognised challenge in generalising PINNs to different subjects, we propose to use PointNet as the nodal-permutation-invariant feature extractor, together with a registration algorithm that aligns point sets and simultaneously takes into account the PINN-imposed biomechanics. Using 77 pairs of MR and ultrasound images from real clinical prostate cancer biopsy, we first demonstrate the efficacy of the proposed registration algorithms in an “unsupervised” subject-specific manner for reducing the target registration error (TRE) compared to that without PINNs especially for patients with large deformations. The improvements stem from the intended biomechanical characteristics being regularised, e.g., the resulting deformation magnitude in rigid transition zones was effectively modulated to be smaller than that in softer peripheral zones. This is further validated to achieve low registration error values of 1.90±0.52 mm and 1.94±0.59 mm for all and surface nodes, respectively, based on ground-truth computed using finite element methods. We then extend and validate the PINN-constrained registration network that can generalise to new subjects. The trained network reduced the rigid-to-soft-region ratio of rigid-excluded deformation magnitude from 1.35±0.15, without PINNs, to 0.89±0.11 (p< 0.001 ) on unseen holdout subjects, which also witnessed decreased TREs from 6.96±1.90 mm to 6.12±1.95 mm (p= 0.018 ). The codes are available at https://github.com/ZheMin-1992/Registration_PINNs
Pharmacokinetics of intravitreal antibiotics in endophthalmitis
Intravitreal antibiotics are the mainstay of treatment in the management of infectious endophthalmitis. Basic knowledge of the commonly used intravitreal antibiotics, which includes their pharmacokinetics, half-life, duration of action and clearance, is essential for elimination of intraocular infection without any iatrogenic adverse effect to the ocular tissue. Various drugs have been studied over the past century to achieve this goal. We performed a comprehensive review of the antibiotics which have been used for intravitreal route and the pharmacokinetic factors influencing the drug delivery and safety profile of these antibiotics. Using online resources like PubMed and Google Scholar, articles were reviewed. The articles were confined to the English language only. We present a broad overview of pharmacokinetic concepts fundamental for use of intravitreal antibiotics in endophthalmitis along with a tabulated compendium of the intravitreal antibiotics using available literature. Recent advances for increasing bioavailability of antibiotics to the posterior segment with the development of controlled drug delivery devices are also described