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Holistic Decomposition Convolution for Effective Semantic Segmentation of 3D MR Images [article]

Guodong Zeng, Guoyan Zheng
2018 arXiv   pre-print
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

Jianghong Zhao, Yinrui Wang, Yuee Cao, Ming Guo, Xianfeng Huang, Ruiju Zhang, Xintong Dou, Xinyu Niu, Yuanyuan Cui, Jun Wang
2021 Remote Sensing  
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]

Jessica M. Bugeja, Ying Xia, Shekhar S. Chandra, Nicholas J. Murphy, Jillian Eyles, Libby Spiers, Stuart Crozier, David J. Hunter, Jurgen Fripp, Craig Engstrom
2021 arXiv   pre-print
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

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
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  
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

G. Anthony Reina, Ravi Panchumarthy, Siddhesh Pravin Thakur, Alexei Bastidas, Spyridon Bakas
2020 Frontiers in Neuroscience  
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]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
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

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
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

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
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

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
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

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
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

Chaitra Dayananda, Jae-Young Choi, Bumshik Lee
2021 Sensors  
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]

Abolfazl Razi, Xiwen Chen, Huayu Li, Hao Wang, Brendan Russo, Yan Chen, Hongbin Yu
2022 arXiv   pre-print
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  
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  
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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