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Holistic Decomposition Convolution for Effective Semantic Segmentation of 3D MR Images
[article]
2018
arXiv
pre-print
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In this paper, we propose a novel Holistic Decomposition Convolution (HDC), for an effective and efficient semantic segmentation of volumetric images. ...
This has motivated the development of 3D CNNs for volumetric image segmentation in order to benefit from more spatial context. ...
holistic decomposition convolution for improving semantic segmentation systems. ...
arXiv:1812.09834v1
fatcat:z4cscuuwtzhg5d3kwvqlk5gyqu
The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review
2021
Remote Sensing
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Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. ...
However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs13204029
fatcat:onnjeqvwb5gsjcrhdaq6hiekru
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
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MR) images in patients with FAI. ...
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 ( ...
Holistic decomposition convolution for effective semantic segmentation of
medical volume images. Medical Image Analysis 2019;57:149-164.
33. Damopoulos D, Lerch TD, Schmaranzer F, et al. ...
arXiv:2112.02723v1
fatcat:z5uk5gztjrb6jb3twimlgzgeku
Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
2020
Brain Sciences
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In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. ...
Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/brainsci10020118
pmid:32098333
pmcid:PMC7071415
fatcat:wofq4puvcbemlconbz6carsf2y
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
Wan, J., +, TIP 2021 121-133 Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Systematic Evaluation of Image Tiling Adverse Effects on Deep Learning Semantic Segmentation
2020
Frontiers in Neuroscience
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Finally, we compare 2D and 3D semantic segmentation models to show that providing CNN models with a wider context of the image in all three dimensions leads to more accurate and consistent predictions. ...
Convolutional neural network (CNN) models obtain state of the art performance on image classification, localization, and segmentation tasks. ...
The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH. ...
doi:10.3389/fnins.2020.00065
pmid:32116512
pmcid:PMC7020775
fatcat:sytg2fbv7radhbipbgfntzupay
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
[article]
2019
arXiv
pre-print
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provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future. ...
This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in 'Medical Imaging with Deep Learning' in the year 2018. ...
[146] built a 3D FCN model for automatic semantic segmentation of 3D images. ...
arXiv:1902.05655v1
fatcat:mjplenjrprgavmy5ssniji4cam
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
2019
IEEE Access
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promising directions for the Medical Imaging Community to fully harness deep learning in the future. ...
This technology has recently attracted so much interest of the Medical Imaging Community that it led to a specialized conference in "Medical Imaging with Deep Learning" in the year 2018. ...
[156] built a 3D FCN model for automatic semantic segmentation of 3D images. ...
doi:10.1109/access.2019.2929365
fatcat:arimcbjaxrd3zcsjyzd7abjgd4
Deep learning in medical imaging and radiation therapy
2018
Medical Physics (Lancaster)
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We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods ...
that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. ...
segmentation of organs is holistically nested networks (HNN). ...
doi:10.1002/mp.13264
pmid:30367497
fatcat:bottst5mvrbkfedbuocbrstcnm
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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Segmentation of 3D Point Clouds: Instance
Separation and Semantic Fusion
DAY 3 -Jan 14, 2021
Zhong, Min; Zeng, Gang
1831
Enhanced Vote Network for 3D Object Detection in Point Clouds
DAY 3 -Jan ...
Point-Based
Semantic Segmentation
DAY 3 -Jan 14, 2021
Liu, Yue; Lian, Zhichao
403
PSDNet: A Balanced Architecture of Accuracy and Parameters for
Semantic Segmentation
DAY 3 -Jan 14, 2021
Song ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
An overview of deep learning in medical imaging focusing on MRI
2018
Zeitschrift für Medizinische Physik
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retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging ...
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. ...
Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article. ...
doi:10.1016/j.zemedi.2018.11.002
fatcat:kkimovnwcrhmth7mg6h6cpomjm
Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation
2021
Sensors
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Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. ...
This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21103363
pmid:34066042
fatcat:qlmzlwho3zfjnc4y24fnjxgrlu
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review
[article]
2022
arXiv
pre-print
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This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed ...
a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. ...
of this project. ...
arXiv:2203.10939v2
fatcat:oml733wvjfh3blne4h7kg5y3du
CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021
2021
International Journal of Computer Assisted Radiology and Surgery
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Novel morphology analysis requires a patient-specific 3D model which is generated from the semantic segmentation of a medical image data of the patient. ...
Semantic segmentation (SS) is one of the labeling methods that associate each pixel of an image with a class label. ...
doi:10.1007/s11548-021-02375-4
pmid:34085172
fatcat:6d564hsv2fbybkhw4wvc7uuxcy
CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020
2020
International Journal of Computer Assisted Radiology and Surgery
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The traditional platforms of CARS Congresses for the scholarly publication and communication process for the presentation of R&D ideas were congress centers or hotels, typically hosting 600-800 participants ...
Aiming to stimulate complimentary thoughts and actions on what is being presented at CARS, implies a number of enabling variables for optimal analogue scholarly communication, such as (examples given are ...
We thank NVIDIA for the Titan X hardware grant that allowed us to process the images in a faster way. ]. ...
doi:10.1007/s11548-020-02171-6
pmid:32514840
fatcat:lyhdb2zfpjcqbf4mmbunddwroq
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