145 research outputs found
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Despite tremendous efforts, it is very challenging to generate a robust model
to assist in the accurate quantification assessment of COVID-19 on chest CT
images. Due to the nature of blurred boundaries, the supervised segmentation
methods usually suffer from annotation biases. To support unbiased lesion
localisation and to minimise the labeling costs, we propose a data-driven
framework supervised by only image-level labels. The framework can explicitly
separate potential lesions from original images, with the help of a generative
adversarial network and a lesion-specific decoder. Experiments on two COVID-19
datasets demonstrate the effectiveness of the proposed framework and its
superior performance to several existing methods.Comment: accepted by ISBI 202
Label-Free Segmentation of COVID-19 Lesions in Lung CT
Scarcity of annotated images hampers the building of automated solution for
reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of
data annotation, we herein present a label-free approach for segmenting
COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the
relevant knowledge from normal CT lung scans. Our modeling is inspired by the
observation that the parts of tracheae and vessels, which lay in the
high-intensity range where lesions belong to, exhibit strong patterns. To
facilitate the learning of such patterns at a pixel level, we synthesize
`lesions' using a set of surprisingly simple operations and insert the
synthesized `lesions' into normal CT lung scans to form training pairs, from
which we learn a normalcy-converting network (NormNet) that turns an 'abnormal'
image back to normal. Our experiments on three different datasets validate the
effectiveness of NormNet, which conspicuously outperforms a variety of
unsupervised anomaly detection (UAD) methods.Comment: Accepted by Transaction on Medical Imaging 202
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
The novel coronavirus disease 2019 (COVID-19) characterized by atypical
pneumonia has caused millions of deaths worldwide. Automatically segmenting
lesions from chest Computed Tomography (CT) is a promising way to assist
doctors in COVID-19 screening, treatment planning, and follow-up monitoring.
However, voxel-wise annotations are extremely expert-demanding and scarce,
especially when it comes to novel diseases, while an abundance of unlabeled
data could be available. To tackle the challenge of limited annotations, in
this paper, we propose an uncertainty-guided dual-consistency learning network
(UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images.
Specifically, we present a dual-consistency learning scheme that simultaneously
imposes image transformation equivalence and feature perturbation invariance to
effectively harness the knowledge from unlabeled data. We then quantify the
segmentation uncertainty in two forms and employ them together to guide the
consistency regularization for more reliable unsupervised learning. Extensive
experiments showed that our proposed UDC-Net improves the fully supervised
method by 6.3% in Dice and outperforms other competitive semi-supervised
approaches by significant margins, demonstrating high potential in real-world
clinical practice.Comment: Accepted to MICCAI2021. The first two authors contributed equally.
Code is available at https://github.com/poiuohke/UDC-Ne
COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory
infection that has had devastating effects on the world. Recently, new COVID-19
variants are emerging making the situation more challenging and threatening.
Evaluation and quantification of COVID-19 lung abnormalities based on chest
Computed Tomography (CT) scans can help determining the disease stage,
efficiently allocating limited healthcare resources, and making informed
treatment decisions. During pandemic era, however, visual assessment and
quantification of COVID-19 lung lesions by expert radiologists become expensive
and prone to error, which raises an urgent quest to develop practical
autonomous solutions. In this context, first, the paper introduces an open
access COVID-19 CT segmentation dataset containing 433 CT images from 82
patients that have been annotated by an expert radiologist. Second, a Deep
Neural Network (DNN)-based framework is proposed, referred to as the
COVID-Rate, that autonomously segments lung abnormalities associated with
COVID-19 from chest CT scans. Performance of the proposed COVID-Rate framework
is evaluated through several experiments based on the introduced and external
datasets. The results show a dice score of 0:802 and specificity and
sensitivity of 0:997 and 0:832, respectively. Furthermore, the results indicate
that the COVID-Rate model can efficiently segment COVID-19 lesions in both 2D
CT images and whole lung volumes. Results on the external dataset illustrate
generalization capabilities of the COVID-Rate model to CT images obtained from
a different scanner
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
COVID-19 (Coronavirus disease 2019) has been quickly spreading since its
outbreak, impacting financial markets and healthcare systems globally.
Countries all around the world have adopted a number of extraordinary steps to
restrict the spreading virus, where early COVID-19 diagnosis is essential.
Medical images such as X-ray images and Computed Tomography scans are becoming
one of the main diagnostic tools to combat COVID-19 with the aid of deep
learning-based systems. In this survey, we investigate the main contributions
of deep learning applications using medical images in fighting against COVID-19
from the aspects of image classification, lesion localization, and severity
quantification, and review different deep learning architectures and some image
preprocessing techniques for achieving a preciser diagnosis. We also provide a
summary of the X-ray and CT image datasets used in various studies for COVID-19
detection. The key difficulties and potential applications of deep learning in
fighting against COVID-19 are finally discussed. This work summarizes the
latest methods of deep learning using medical images to diagnose COVID-19,
highlighting the challenges and inspiring more studies to keep utilizing the
advantages of deep learning to combat COVID-19
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Consistent segmentation of COVID-19 patient's CT scans across multiple time
points is essential to assess disease progression and response to therapy
accurately. Existing automatic and interactive segmentation models for medical
images only use data from a single time point (static). However, valuable
segmentation information from previous time points is often not used to aid the
segmentation of a patient's follow-up scans. Also, fully automatic segmentation
techniques frequently produce results that would need further editing for
clinical use. In this work, we propose a new single network model for
interactive segmentation that fully utilizes all available past information to
refine the segmentation of follow-up scans. In the first segmentation round,
our model takes 3D volumes of medical images from two-time points (target and
reference) as concatenated slices with the additional reference time point
segmentation as a guide to segment the target scan. In subsequent segmentation
refinement rounds, user feedback in the form of scribbles that correct the
segmentation and the target's previous segmentation results are additionally
fed into the model. This ensures that the segmentation information from
previous refinement rounds is retained. Experimental results on our in-house
multiclass longitudinal COVID-19 dataset show that the proposed model
outperforms its static version and can assist in localizing COVID-19 infections
in patient's follow-up scans.Comment: 10 pages, 11 figures, 4 table
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
The pandemic of COVID-19 has caused millions of infections, which has led to
a great loss all over the world, socially and economically. Due to the
false-negative rate and the time-consuming of the conventional Reverse
Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on
X-ray images and Computed Tomography (CT) images has been widely adopted.
Therefore, researchers of the computer vision area have developed many
automatic diagnosing models based on machine learning or deep learning to
assist the radiologists and improve the diagnosing accuracy. In this paper, we
present a review of these recently emerging automatic diagnosing models. 70
models proposed from February 14, 2020, to July 21, 2020, are involved. We
analyzed the models from the perspective of preprocessing, feature extraction,
classification, and evaluation. Based on the limitation of existing models, we
pointed out that domain adaption in transfer learning and interpretability
promotion would be the possible future directions.Comment: In ICRAI 2020: 2020 6th International Conference on Robotics and
Artificial Intelligenc
Detection and severity classification of COVID-19 in CT images using deep learning
Since the breakout of coronavirus disease (COVID-19), the computer-aided
diagnosis has become a necessity to prevent the spread of the virus. Detecting
COVID-19 at an early stage is essential to reduce the mortality risk of the
patients. In this study, a cascaded system is proposed to segment the lung,
detect, localize, and quantify COVID-19 infections from computed tomography
(CT) images Furthermore, the system classifies the severity of COVID-19 as
mild, moderate, severe, or critical based on the percentage of infected lungs.
An extensive set of experiments were performed using state-of-the-art deep
Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature
Pyramid Network (FPN), with different backbone (encoder) structures using the
variants of DenseNet and ResNet. The conducted experiments showed the best
performance for lung region segmentation with Dice Similarity Coefficient (DSC)
of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with
the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant
performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of
91.85% using the FPN model with the DenseNet201 encoder. The achieved
performance is significantly superior to previous methods for COVID-19 lesion
localization. Besides, the proposed system can reliably localize infection of
various shapes and sizes, especially small infection regions, which are rarely
considered in recent studies. Moreover, the proposed system achieved high
COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity.
Finally, the system was able to discriminate between different severity levels
of COVID-19 infection over a dataset of 1,110 subjects with sensitivity values
of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical
infections, respectively.Comment: 9 Figures, 8 Table
Do not repeat these mistakes -- a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the
most important global problems today. In a short period of time, it has led to
the development of many deep neural network models for COVID-19 detection with
modules for explainability. In this work, we carry out a systematic analysis of
various aspects of proposed models. Our analysis revealed numerous mistakes
made at different stages of data acquisition, model development, and
explanation construction. In this work, we overview the approaches proposed in
the surveyed ML articles and indicate typical errors emerging from the lack of
deep understanding of the radiography domain. We present the perspective of
both: experts in the field - radiologists, and deep learning engineers dealing
with model explanations. The final result is a proposed a checklist with the
minimum conditions to be met by a reliable COVID-19 diagnostic model
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
Recent research on COVID-19 suggests that CT imaging provides useful
information to assess disease progression and assist diagnosis, in addition to
help understanding the disease. There is an increasing number of studies that
propose to use deep learning to provide fast and accurate quantification of
COVID-19 using chest CT scans. The main tasks of interest are the automatic
segmentation of lung and lung lesions in chest CT scans of confirmed or
suspected COVID-19 patients. In this study, we compare twelve deep learning
algorithms using a multi-center dataset, including both open-source and
in-house developed algorithms. Results show that ensembling different methods
can boost the overall test set performance for lung segmentation, binary lesion
segmentation and multiclass lesion segmentation, resulting in mean Dice scores
of 0.982, 0.724 and 0.469, respectively. The resulting binary lesions were
segmented with a mean absolute volume error of 91.3 ml. In general, the task of
distinguishing different lesion types was more difficult, with a mean absolute
volume difference of 152 ml and mean Dice scores of 0.369 and 0.523 for
consolidation and ground glass opacity, respectively. All methods perform
binary lesion segmentation with an average volume error that is better than
visual assessment by human raters, suggesting these methods are mature enough
for a large-scale evaluation for use in clinical practice.Comment: Updated acknowledgment
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