1,662 research outputs found
Multiple landmark detection using multi-agent reinforcement learning
The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naïve approach of training K agents separately. Code and visualizations available: https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detectio
Curriculum deep reinforcement learning with different exploration strategies : a feasibility study on cardiac landmark detection
Transcatheter aortic valve implantation (TAVI) is associated with conduction abnormalities and the mechanical interaction between the prosthesis and the atrioventricular (AV) conduction path cause these life-threatening arrhythmias. Pre-operative assessment of the location of the AV conduction path can help to understand the risk of post-TAVI conduction abnormalities. As the AV conduction path is not visible on cardiac CT, the inferior border of the membranous septum can be used as an anatomical landmark. Detecting this border automatically, accurately and efficiently would save operator time and thus benefit pre-operative planning. This preliminary study was performed to identify the feasibility of 3D landmark detection in cardiac CT images with curriculum deep Q-learning. In this study, curriculum learning was used to gradually teach an artificial agent to detect this anatomical landmark from cardiac CT. This agent was equipped with a small field of view and burdened with a large ac tion-space. Moreover, we introduced two novel action-selection strategies: α-decay and action-dropout. We compared these two strategies to the already established ε-decay strategy and observed that α-decay yielded the most accurate results. Limited computational resources were used to ensure reproducibility. In order to maximize the amount of patient data, the method was cross-validated with k-folding for all three action-selection strategies. An inter-operator variability study was conducted to assess the accuracy of the metho
Towards autonomous diagnostic systems with medical imaging
Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities.
This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed.
Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way.
In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones.
By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos.
All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images
In this study, we propose a fast and accurate method to automatically
localize anatomical landmarks in medical images. We employ a global-to-local
localization approach using fully convolutional neural networks (FCNNs). First,
a global FCNN localizes multiple landmarks through the analysis of image
patches, performing regression and classification simultaneously. In
regression, displacement vectors pointing from the center of image patches
towards landmark locations are determined. In classification, presence of
landmarks of interest in the patch is established. Global landmark locations
are obtained by averaging the predicted displacement vectors, where the
contribution of each displacement vector is weighted by the posterior
classification probability of the patch that it is pointing from. Subsequently,
for each landmark localized with global localization, local analysis is
performed. Specialized FCNNs refine the global landmark locations by analyzing
local sub-images in a similar manner, i.e. by performing regression and
classification simultaneously and combining the results. Evaluation was
performed through localization of 8 anatomical landmarks in CCTA scans, 2
landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We
demonstrate that the method performs similarly to a second observer and is able
to localize landmarks in a diverse set of medical images, differing in image
modality, image dimensionality, and anatomical coverage.Comment: 12 pages, accepted at IEEE transactions in Medical Imagin
Strategising template-guided needle placement for MR-targeted prostate biopsy
Clinically significant prostate cancer has a better chance to be sampled
during ultrasound-guided biopsy procedures, if suspected lesions found in
pre-operative magnetic resonance (MR) images are used as targets. However, the
diagnostic accuracy of the biopsy procedure is limited by the
operator-dependent skills and experience in sampling the targets, a sequential
decision making process that involves navigating an ultrasound probe and
placing a series of sampling needles for potentially multiple targets. This
work aims to learn a reinforcement learning (RL) policy that optimises the
actions of continuous positioning of 2D ultrasound views and biopsy needles
with respect to a guiding template, such that the MR targets can be sampled
efficiently and sufficiently. We first formulate the task as a Markov decision
process (MDP) and construct an environment that allows the targeting actions to
be performed virtually for individual patients, based on their anatomy and
lesions derived from MR images. A patient-specific policy can thus be
optimised, before each biopsy procedure, by rewarding positive sampling in the
MDP environment. Experiment results from fifty four prostate cancer patients
show that the proposed RL-learned policies obtained a mean hit rate of 93% and
an average cancer core length of 11 mm, which compared favourably to two
alternative baseline strategies designed by humans, without hand-engineered
rewards that directly maximise these clinically relevant metrics. Perhaps more
interestingly, it is found that the RL agents learned strategies that were
adaptive to the lesion size, where spread of the needles was prioritised for
smaller lesions. Such a strategy has not been previously reported or commonly
adopted in clinical practice, but led to an overall superior targeting
performance when compared with intuitively designed strategies.Comment: Paper submitted and accepted to CaPTion (Cancer Prevention through
early detecTion) @ MICCAI 2022 Worksho
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