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Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from 3D magnetic resonance images
[article]
2021
arXiv
pre-print
Preserved Fulltext
The purpose of this study is to implement a novel, automated three-dimensional (3D) pipeline, CamMorph, for segmentation and measurement of cam volume, surface area and height from magnetic resonance ( ...
Automated analyses of 3D MR images from patients with FAI using the CamMorph pipeline showed that, in comparison with female patients, male patients had significantly greater cam volume, surface area and ...
Deep Volumetric Shape Learning for Semantic Segmentation of the Hip
Joint from 3D MR Images. Computational Methods and Clinical Applications in
Musculoskeletal Imaging. ...
arXiv:2112.02723v1
fatcat:z5uk5gztjrb6jb3twimlgzgeku
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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and artifact removal in arterial spin labelling MRI 680 Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences 681 3D Segmentation with Exponential Logarithmic Loss for Highly ...
Semi-supervised learning for segmentation under semantic constraint 608 A Decomposable Model for the Detection of Prostate Cancer in Multi-Parametric MRI 609 Deep nested level sets: Fully automated segmentation ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Holistic Decomposition Convolution for Effective Semantic Segmentation of 3D MR Images
[article]
2018
arXiv
pre-print
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This has motivated the development of 3D CNNs for volumetric image segmentation in order to benefit from more spatial context. ...
Results obtained from comprehensive experiments conducted on hip T1 MR images and intervertebral disc T2 MR images demonstrate the efficacy of the present approach. ...
Ablation study on hip MR images with limited field of view Data and augmentation In this study, we used 25 3D T1 MR images, acquired from patients with hip pain. ...
arXiv:1812.09834v1
fatcat:z4cscuuwtzhg5d3kwvqlk5gyqu
Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
[article]
2020
arXiv
pre-print
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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 ...
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. ...
of the knee, hip and shoulder joint. ...
arXiv:2006.15578v2
fatcat:txa2zt4d35dg3apyecnsoglliy
SUSAN: Segment Unannotated image Structure using Adversarial Network
[article]
2018
arXiv
pre-print
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The proposed method was evaluated for segmenting bone and cartilage on two clinical knee MR image datasets acquired at our institution using only a single set of annotated data from a publicly available ...
The recent implementation of deep convolutional neural networks (CNNs) in image processing has been shown to have significant impacts on medical image segmentation. ...
For example, it would be useful to translate knee joint cartilage segmentation into cartilage segmentation of the hip joint since annotating hip joint cartilage is extremely difficult due to the thin cartilage ...
arXiv:1812.00555v1
fatcat:qho2mi6u3vfbzok46fm7kugv3u
Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears
2022
Sensors
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This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. ...
The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images. ...
Discussion In this study, we focused on the semantic segmentation deep learning approach of U-Net CNN architecture to segment the ACL MR images automatically. ...
doi:10.3390/s22041552
pmid:35214451
pmcid:PMC8876207
fatcat:ibkfflw7dbfxrcfozpwwlnxtca
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)
[article]
2021
arXiv
pre-print
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2022 accepted arXiv:2107.12889v2 fatcat:qxskzf6vuzgblpvourpa7gy7da
Segmentation of bone, cartilage, and joint fluid is necessary for the OA objective assessment. ...
Training and validation of the method were performed using 500 MRI knees from the Osteoarthritis Initiative (OAI) dataset and 97 MRI scans of patients with symptomatic hip OA. ...
., 2017) reported accuracy score 62% for a slice-based segmentation of lower limb MR images using the Segnet CNN architecture followed by 3D deformable model postprocessing. Shah et al. ...
arXiv:2107.12889v1
fatcat:onyo2dfyevdadfu3ai244rjvea
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
[article]
2022
arXiv
pre-print
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The proposed method is evaluated for the task of multi-bone segmentation on two scarce pediatric imaging datasets from ankle and shoulder joints, comprising pathological as well as healthy examinations ...
We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. ...
Christelle Pons from Ildys Foundation, Brest, France for her involvement in clinical data collection and patient enrolment process. ...
arXiv:2009.07092v5
fatcat:dpd2vsoipnejnbzgxqt4rmc6cm
Advances in Deep Learning-Based Medical Image Analysis
2021
Health Data Science
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With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active ...
This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. ...
Acknowledgments This study was supported in part by grants from the Zhejiang Provincial Key Research & Development Program (No. 2020C03073). ...
doi:10.34133/2021/8786793
fatcat:d6nkb4yoxrcgni4y5owju5pnh4
Narrative review of generative adversarial networks in medical and molecular imaging
2021
Annals of Translational Medicine
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For example, Hiasa et al. performed a conversion in 2D from MR to CT images by applying a CycleGAN technique trained with T1W MR and CT images of lower abdominal regions including the hip joints (29) ...
Unsupervised domain adaptation using adversarial training of 3D multi-scale CNNs was demonstrated for the segmentation of traumatic brain injuries (TBI) on brain MR images (51) . ...
Footnote Provenance and Peer Review: This article was commissioned by the editorial office, Annals of Translational Medicine for the series "Artificial Intelligence in Molecular Imaging". ...
doi:10.21037/atm-20-6325
fatcat:6dfpalmijjcrnmlb7e6ppycloq
Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation
2019
Frontiers in Neuroinformatics
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Accurate and automatic segmentation of infant hippocampal subfields from magnetic resonance (MR) images is an important step for studying memory related infant neurological diseases. ...
3D U-net. ...
ACKNOWLEDGMENTS This work utilizes data collected by a NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium. ...
doi:10.3389/fninf.2019.00030
pmid:31068797
pmcid:PMC6491864
fatcat:e7cnqfplbjhj3itzbkcwii7fvy
Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion
2020
Physics in Medicine and Biology
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In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. ...
Its performance was compared to the 3-class segmentation-based AC approach (containing background air, soft-tissue and lung) obtained from MR images. ...
of the European Commission under grant E! ...
doi:10.1088/1361-6560/abb02c
pmid:32976116
fatcat:zvmjo37sgbdnjffs5xxy4ewwcm
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
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. ...
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
Acknowledgments The authors would like to thank members of the Diagnostic Image Analysis Group for discussions and suggestions. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
2019 Index IEEE Robotics and Automation Letters Vol. 4
2019
IEEE Robotics and Automation Letters
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., +, LRA Oct. 2019 3153-3160 Variational Object-Aware 3-D Hand Pose From a Single RGB Image. Gao, Y., +, LRA Oct. 2019 4239-4246 Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery. ...
., +, LRA Oct. 2019 3153-3160 Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery. ...
Permanent magnets Adaptive Dynamic Control for Magnetically Actuated Medical Robots. ...
doi:10.1109/lra.2019.2955867
fatcat:ckastwefh5chhamsravandtnx4
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
[article]
2018
arXiv
pre-print
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models to the most recent deep learning-based approaches. ...
The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution ...
Acknowledgements This paper was supported in part by the Marie Skodoska-Curie Actions of the UE Framework Program for Research and Innovation, under REA grant agreement 706372. ...
arXiv:1812.08577v1
fatcat:xjw2g25pxfftpnduss6d5sggzu
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