777 research outputs found
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
Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis
Objectives
The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images.
Methods
PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication.
Results
The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I-2 = 98.13%, tau(2) = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012).
Conclusion
Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done
Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images
Radiotherapists require accurate registration of MR/CT images to effectively
use information from both modalities. In a typical registration pipeline, rigid
or affine transformations are applied to roughly align the fixed and moving
images before proceeding with the deformation step. While recent learning-based
methods have shown promising results in the rigid/affine step, these methods
often require images with similar field-of-view (FOV) for successful alignment.
As a result, aligning images with different FOVs remains a challenging task.
Self-supervised landmark detection methods like self-supervised Anatomical
eMbedding (SAM) have emerged as a useful tool for mapping and cropping images
to similar FOVs. However, these methods are currently limited to intra-modality
use only. To address this limitation and enable cross-modality matching, we
propose a new approach called Cross-SAM. Our approach utilizes a novel
iterative process that alternates between embedding learning and CT-MRI
registration. We start by applying aggressive contrast augmentation on both CT
and MRI images to train a SAM model. We then use this SAM to identify
corresponding regions on paired images using robust grid-points matching,
followed by a point-set based affine/rigid registration, and a deformable
fine-tuning step to produce registered paired images. We use these registered
pairs to enhance the matching ability of SAM, which is then processed
iteratively. We use the final model for cross-modality matching tasks. We
evaluated our approach on two CT-MRI affine registration datasets and found
that Cross-SAM achieved robust affine registration on both datasets,
significantly outperforming other methods and achieving state-of-the-art
performance
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
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
Clinical research on smart healthcare has an increasing demand for
intelligent and clinic-oriented medical image computing algorithms and
platforms that support various applications. To this end, we have developed
SenseCare research platform for smart healthcare, which is designed to boost
translational research on intelligent diagnosis and treatment planning in
various clinical scenarios. To facilitate clinical research with Artificial
Intelligence (AI), SenseCare provides a range of AI toolkits for different
tasks, including image segmentation, registration, lesion and landmark
detection from various image modalities ranging from radiology to pathology. In
addition, SenseCare is clinic-oriented and supports a wide range of clinical
applications such as diagnosis and surgical planning for lung cancer, pelvic
tumor, coronary artery disease, etc. SenseCare provides several appealing
functions and features such as advanced 3D visualization, concurrent and
efficient web-based access, fast data synchronization and high data security,
multi-center deployment, support for collaborative research, etc. In this
paper, we will present an overview of SenseCare as an efficient platform
providing comprehensive toolkits and high extensibility for intelligent image
analysis and clinical research in different application scenarios.Comment: 11 pages, 10 figure
Automatic detection of the aortic annular plane and coronary ostia from multidetector computed tomography
Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1-2.1], 2.0 mm [1.3-2.8] with a paired difference -0.5 +/- 1.3 mm and p value <0.001). Furthermore, a high correlation is observed between predicted and manually measured coronary heights (for both R-2 = 0.8). The image analysis time per patient was below one second. The proposed method is accurate, fast, and reproducible. Embedding this tool based on deep learning in the preoperative planning routine may have an impact in the TAVI environments by reducing the time and cost and improving accuracy
Deep learning for cephalometric landmark detection: systematic review and meta-analysis
Objectives: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs.
Methods: Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498).
Data: From 321 identified records, 19 studies (published 2017-2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7-93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (-0.581; 95 CI: -1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824).
Conclusions: DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed.
Clinical significance: Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective
3D cephalometric landmark detection by multiple stage deep reinforcement learning
The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.ope
3D Deep Learning on Medical Images: A Review
The rapid advancements in machine learning, graphics processing technologies
and availability of medical imaging data has led to a rapid increase in use of
deep learning models in the medical domain. This was exacerbated by the rapid
advancements in convolutional neural network (CNN) based architectures, which
were adopted by the medical imaging community to assist clinicians in disease
diagnosis. Since the grand success of AlexNet in 2012, CNNs have been
increasingly used in medical image analysis to improve the efficiency of human
clinicians. In recent years, three-dimensional (3D) CNNs have been employed for
analysis of medical images. In this paper, we trace the history of how the 3D
CNN was developed from its machine learning roots, give a brief mathematical
description of 3D CNN and the preprocessing steps required for medical images
before feeding them to 3D CNNs. We review the significant research in the field
of 3D medical imaging analysis using 3D CNNs (and its variants) in different
medical areas such as classification, segmentation, detection, and
localization. We conclude by discussing the challenges associated with the use
of 3D CNNs in the medical imaging domain (and the use of deep learning models,
in general) and possible future trends in the field.Comment: 13 pages, 4 figures, 2 table
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