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Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection
2019
Medical Image Analysis
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Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. ...
In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. ...
Acknowledgments We thank the volunteers, radiographers and experts for providing manually annotated datasets, Wellcome Trust IEH Award [102431], NVIDIA for their GPU donations, and Intel. ...
doi:10.1016/j.media.2019.02.007
pmid:30784956
pmcid:PMC7610752
fatcat:snhtae3iebc2nlld7g5ardsrza
2-step deep learning model for landmarks localization in spine radiographs
2021
Scientific Reports
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AbstractIn this work we propose to use Deep Learning to automatically calculate the coordinates of the vertebral corners in sagittal x-rays images of the thoracolumbar spine and, from those landmarks, ...
For the localization tasks, we used a differentiable spatial to numerical transform (DSNT) as the top layer. We evaluated the model both qualitatively and quantitatively on a set of 195 test images. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. ...
doi:10.1038/s41598-021-89102-w
pmid:33947917
fatcat:uhqwr3odozcfnktylffyyllfx4
A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics
[article]
2019
bioRxiv
pre-print
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Image registration, or the spatial alignment of images, is a fundamental technique in automatic image analysis that is well-poised for such purposes. ...
Using micro-computed tomography images of genetically and morphologically variable mouse skulls, we test our landmarking approach under a variety of registration conditions, including different non-linear ...
In this paper, we combine techniques from machine learning and image registration, or the spatial alignment of images, to automatically and accurately detect landmarks for GM. ...
doi:10.1101/2019.12.11.873182
fatcat:vk4z4yhpxbednjirgfisltdquy
Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks
2017
IEEE Transactions on Image Processing
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One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. ...
between local image patches and target anatomical landmarks. ...
Accordingly, we propose an end-to-end deep learning approach to detect large-scale landmarks in real time, by using limited medical images. Figure 1 briefly illustrates our proposed method. ...
doi:10.1109/tip.2017.2721106
pmid:28678706
pmcid:PMC5729285
fatcat:cb5osyrw2ffepb3hgyd67srggi
Regression forests for efficient anatomy detection and localization in computed tomography scans
2013
Medical Image Analysis
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This paper proposes a new algorithm for the efficient, automatic detection and localization of multiple anatomical structures within three-dimensional computed tomography (CT) scans. ...
The main contribution of this work is a new, continuous parametrization of the anatomy localization problem, which allows it to be addressed effectively by multi-class random regression forests. ...
The database comprises patients with a wide variety of medical conditions and body shapes and the scans exhibit large differences in image cropping, resolution, scanner type, and use of contrast agents ...
doi:10.1016/j.media.2013.01.001
pmid:23410511
fatcat:4wqymw5vqjgifmddqemiudvz4u
Automatic Analysis of Facial Actions: A Survey
2017
IEEE Transactions on Affective Computing
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Image/Video Appearance-based approaches, Geometry-based approaches, Motion-based approaches, Hybrid approaches Face detection and tracking, Facial point detection and tracking, Face normalisation AU classification ...
of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face. ...
For example, the kernel Conditional Ordinal Random Fields was applied to the AU temporal segment detection problem in [141] , and makes use of the temporal ordering constraints of the labels. ...
doi:10.1109/taffc.2017.2731763
fatcat:6qp6zg5rrrgwrladsnwmyeqbq4
Deep Learning in Medical Imaging
2019
Neurospine
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In this review article, we will explain the history, development, and applications in medical imaging. ...
The advancement of computing power with graphics processing units and the availability of large data acquisition, deep neural network outperforms human or other ML capabilities in computer vision and speech ...
They compared the performance of the Markov random field method to that of CNN and showed the CNN network can be used in denoising. Not only CNN but also autoencoder (AE) can be used in denoising. ...
doi:10.14245/ns.1938396.198
pmid:31905454
pmcid:PMC6945006
fatcat:miszi3fiojh35ldsgggxgpaowa
Facial Landmark Detection: A Literature Survey
2018
International Journal of Computer Vision
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2018 pre-print arXiv:1805.05563v1 fatcat:inttnpcrlrfvho6d5l2q5qpdwi
Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. ...
The locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. ...
For example, Markov Random Field (MRF) model is used in [21] , and Dynamic Bayesian Network is used in [56] . ...
doi:10.1007/s11263-018-1097-z
fatcat:ykqg6lr3j5bbrmrmli2dlrxupi
On the Methods for Detecting Brain Tumor from MRI images
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. ...
The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. ...
So automatic detection became important and this study explores many methods used for detection and segmentation of tumor from MRI images. ...
doi:10.35940/ijitee.i1007.0799s20
fatcat:mpo2gpnwwvhv3fj74idihf4uom
Shaping the Future through Innovations: From Medical Imaging to Precision Medicine
[article]
2016
arXiv
pre-print
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Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up. ...
This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth ...
We would also like to thank our clinical and non-clinical collaborators for the strong support over many years.
Disclaimer This feature is based on research, and is not commercially available. ...
arXiv:1605.02029v2
fatcat:kwsyfx5jqrfuhlblnvnd4pf2n4
Facial Landmark Detection via Attention-Adaptive Deep Network
2019
IEEE Access
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Facial landmark detection is a key component of the face recognition pipeline as well as facial attribute analysis and face verification. ...
It can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. ...
These points also can be centred at common facial components. Facial analysis tasks can differ in numbers and types, in terms of number of needed facial points, and use of these facial points. ...
doi:10.1109/access.2019.2955156
fatcat:rkyphd4rmvfulcjajiipyuogbe
3D cephalometric landmark detection by multiple stage deep reinforcement learning
2021
Scientific Reports
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We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. ...
Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. ...
., and K.J. S.H.K. and K.J. were partially supported by the National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government (No. NIMS-B21910000). ...
doi:10.1038/s41598-021-97116-7
pmid:34471202
fatcat:rjrvbhtaozekdokyq3q7mif7me
Tracked Ultrasound in Navigated Spine Interventions
[chapter]
2014
Lecture Notes in Computational Vision and Biomechanics
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Ultrasound is an increasingly popular imaging modality in image-guided interventions, due to its safety, accessibility, and low cost. ...
But ultrasound imaging has a steep learning curve, and requires significant coordination skills from the operator. ...
Position of the water tank bottom is automatically detected in the ultrasound image and used as position signal for the image data. ...
doi:10.1007/978-3-319-12508-4_15
fatcat:uzgt4n7fmzcpxjdthieneqgrlq
Object learning and detection using evolutionary deformable models for mobile robot navigation
2007
Robotica (Cambridge. Print)
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Deformable models have been studied in image analysis over the last decade and used for recognition of flexible or rigid templates under diverse viewing conditions. ...
Instead of receiving the detailed model definition from the user, the algorithm extracts and learns the information from each object automatically. ...
This allows an exact "topological localization," and also confirms the detection of the right landmark. ...
doi:10.1017/s0263574707003633
fatcat:sr74z35pcjdcbpzatakiteifgy
Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach
2021
Diagnostics
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In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. ...
The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. ...
Acknowledgments: Special thanks to Mattias Nicolas Bossa, Kjell Van Royen and Tjeerd Jager for proofreading this manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/diagnostics11112062
pmid:34829409
pmcid:PMC8621122
fatcat:liqbotbkvffnxblilfjedi7lku
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