243 research outputs found
Automated quantification of cartilage quality for hip treatment decision support
Purpose
Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools.
Methods
A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume.
Results
The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed.
Conclusion
An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future
Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
Deep neural networks are parameterised by weights that encode feature
representations, whose performance is dictated through generalisation by using
large-scale feature-rich datasets. The lack of large-scale labelled 3D medical
imaging datasets restrict constructing such generalised networks. In this work,
a novel 3D segmentation network, Fabric Image Representation Networks
(FIRENet), is proposed to extract and encode generalisable feature
representations from multiple medical image datasets in a large-scale manner.
FIRENet learns image specific feature representations by way of 3D fabric
network architecture that contains exponential number of sub-architectures to
handle various protocols and coverage of anatomical regions and structures. The
fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to
extract local and image-level features at a fine selection of scales. The
fabric is constructed with weighted edges allowing the learnt features to
dynamically adapt to the training data at an architecture level. Conditional
padding modules, which are integrated into the network to reinsert voxels
discarded by feature pooling, allow the network to inherently process
different-size images at their original resolutions. FIRENet was trained for
feature learning via automated semantic segmentation of pelvic structures and
obtained a state-of-the-art median DSC score of 0.867. FIRENet was also
simultaneously trained on MR (Magnatic Resonance) images acquired from 3D
examinations of musculoskeletal elements in the (hip, knee, shoulder) joints
and a public OAI knee dataset to perform automated segmentation of bone across
anatomy. Transfer learning was used to show that the features learnt through
the pelvic segmentation helped achieve improved mean DSC scores of 0.962,
0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure
CartiMorph: a framework for automated knee articular cartilage morphometrics
We introduce CartiMorph, a framework for automated knee articular cartilage
morphometrics. It takes an image as input and generates quantitative metrics
for cartilage subregions, including the percentage of full-thickness cartilage
loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the
power of deep learning models for hierarchical image feature representation.
Deep learning models were trained and validated for tissue segmentation,
template construction, and template-to-image registration. We established
methods for surface-normal-based cartilage thickness mapping, FCL estimation,
and rule-based cartilage parcellation. Our cartilage thickness map showed less
error in thin and peripheral regions. We evaluated the effectiveness of the
adopted segmentation model by comparing the quantitative metrics obtained from
model segmentation and those from manual segmentation. The root-mean-squared
deviation of the FCL measurements was less than 8%, and strong correlations
were observed for the mean thickness (Pearson's correlation coefficient ), surface area () and volume () measurements. We compared our FCL measurements with those from a
previous study and found that our measurements deviated less from the ground
truths. We observed superior performance of the proposed rule-based cartilage
parcellation method compared with the atlas-based approach. CartiMorph has the
potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi
Artificial intelligence in musculoskeletal ultrasound imaging
Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.11Nsciescopu
Machine learning in orthopedics: a literature review
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Machine Learning in Orthopedics: A Literature Review
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance
Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including: (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation. When clinically validated, GANs have the potential to improve musculoskeletal imaging.ope
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