6 research outputs found
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images
Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is
a powerful modality in functional and anatomical assessment for various
cardiovascular diseases. Sufficient image quality is essential to achieve
proper diagnosis and treatment. A large number of medical images, the variety
of imaging artefacts, and the workload of imaging centres are among the things
that reveal the necessity of automatic image quality assessment (IQA). However,
automated IQA requires access to bulk annotated datasets for training deep
learning (DL) models. Labelling medical images is a tedious, costly and
time-consuming process, which creates a fundamental challenge in proposing
DL-based methods for medical applications. This study aims to present a new
method for CMR IQA when there is limited access to annotated datasets. Methods:
The proposed generalised deep meta-learning model can evaluate the quality by
learning tasks in the prior stage and then fine-tuning the resulting model on a
small labelled dataset of the desired tasks. This model was evaluated on the
data of over 6,000 subjects from the UK Biobank for five defined tasks,
including detecting respiratory motion, cardiac motion, Aliasing and Gibbs
ringing artefacts and images without artefacts. Results: The results of
extensive experiments show the superiority of the proposed model. Besides,
comparing the model's accuracy with the domain adaptation model indicates a
significant difference by using only 64 annotated images related to the desired
tasks. Conclusion: The proposed model can identify unknown artefacts in images
with acceptable accuracy, which makes it suitable for medical applications and
quality assessment of large cohorts.Comment: 16 pages, 1 figure, 2 table
Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains
Population imaging studies rely upon good quality medical imagery before
downstream image quantification. This study provides an automated approach to
assess image quality from cardiovascular magnetic resonance (CMR) imaging at
scale. We identify four common CMR imaging artefacts, including respiratory
motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with
images acquired in different views, including two, three, and four-chamber
long-axis and short-axis cine CMR images. Two deep learning-based models in
spatial and frequency domains are proposed. Besides recognising these
artefacts, the proposed models are suitable to the common challenges of not
having access to data labels. An unsupervised domain adaptation method and a
Fourier-based convolutional neural network are proposed to overcome these
challenges. We show that the proposed models reliably allow for CMR image
quality assessment. The accuracies obtained for the spatial model in supervised
and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank
dataset, respectively. Using unsupervised domain adaptation can somewhat
overcome the challenge of not having access to the data labels. The maximum
achieved domain gap coverage in unsupervised domain adaptation is 16.86%.
Domain adaptation can significantly improve a 5-class classification task and
deal with considerable domain shift without data labels. Increasing the speed
of training and testing can be achieved with the proposed model in the
frequency domain. The frequency-domain model can achieve the same accuracy yet
1.548 times faster than the spatial model. This model can also be used directly
on k-space data, and there is no need for image reconstruction.Comment: 21 pages, 9 figures, 7 table
Multiple Teachers-Meticulous Student:A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification
Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously
A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria
Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. This paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. The quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. The scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. The Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts' quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. The results show that the quality scoring by the proposed framework is highly correlated with the experts' opinions. The proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank.</p
Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model
Cardiovascular magnetic resonance (CMR) imaging has become a modality with
superior power for the diagnosis and prognosis of cardiovascular diseases. One
of the essential basic quality controls of CMR images is to investigate the
complete cardiac coverage, which is necessary for the volumetric and functional
assessment. This study examines the full cardiac coverage using a 3D
convolutional model and then reduces the number of false predictions using an
innovative salient region detection model. Salient regions are extracted from
the short-axis cine CMR stacks using a three-step proposed algorithm. Combining
the 3D CNN baseline model with the proposed salient region detection model
provides a cascade detector that can reduce the number of false negatives of
the baseline model. The results obtained on the images of over 6,200
participants of the UK Biobank population cohort study show the superiority of
the proposed model over the previous state-of-the-art studies. The dataset is
the largest regarding the number of participants to control the cardiac
coverage. The accuracy of the baseline model in identifying the
presence/absence of basal/apical slices is 96.25\% and 94.51\%, respectively,
which increases to 96.88\% and 95.72\% after improving using the proposed
salient region detection model. Using the salient region detection model by
forcing the baseline model to focus on the most informative areas of the images
can help the model correct misclassified samples' predictions. The proposed
fully automated model's performance indicates that this model can be used in
image quality control in population cohort datasets and also real-time
post-imaging quality assessments