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Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention [article]

Minyoung Chung, Jingyu Lee, Jeongjin Lee, Yeong-Gil Shin
2020 arXiv   pre-print
In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that shows high generalization performance and accuracy.  ...  Further, we extend our network by employing a self-supervised contour scheme.  ...  The remaining 100 images were used for training based on a two-fold crossvalidation (i.e., 50 training images and 50 validation images). We resampled all abdominal CT images into 256 × 256 × 64.  ... 
arXiv:2002.05895v1 fatcat:njhnvrej2nfr5grbxjfxh7xrt4

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

Batch Normalized Convolution Neural Network for Liver Segmentation

Fatima Abdalbagi
2020 Zenodo  
In this paper, we propose a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver CT images using Deep Learning Technique.  ...  Medical Image Computing and Computer Assisted Intervention (MICCAI) dataset and 3DImage Reconstruction for Comparison of Algorithm Database (3D-IRCAD) were used in the experimentation and the average results  ...  They use a self-supervised contour scheme to extend their network. They achieved better accuracy when compared to the state-of-the-art networks by reducing 10.31% of the Hausdorff distance.  ... 
doi:10.5281/zenodo.4264377 fatcat:mm4uzuxkqfcxdp3i4liwwu64pq

3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor

Haimei Li, Bing Liu, Yongtao Zhang, Chao Fu, Xiaowei Han, Lei Du, Wenwen Gao, Yue Chen, Xiuxiu Liu, Yige Wang, Tianfu Wang, Guolin Ma (+1 others)
2021 Frontiers in Oncology  
Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge.  ...  This study designs a novel 3D improved feature pyramidal network (3D IFPN) to automatically segment gastric tumors in computed tomography (CT) images.  ...  ACKNOWLEDGMENTS We sincerely thank all the subjects enrolled in our study.  ... 
doi:10.3389/fonc.2021.618496 pmid:34094903 pmcid:PMC8173118 fatcat:63zl5gbkrrej5lubx7z2syqznm

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.  ...  However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire.  ...  from scratch for lung nodule segmentation and liver segmentation in CT scans, respectively.  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Muhammad Zubair Khan, Mohan Kumar Gajendran, Yugyung Lee, Muazzam A. Khan
2021 IEEE Access  
Besides, [155] has embedded attention gates with a deep network to segment abdominal organs from 2D CT scans. The attention mechanism aids the model to learn organs of variable size and shape.  ...  In [183] , the authors proposed an attention-based U-Net model for segmenting CT-150 and CT-82 datasets with various settings.  ... 
doi:10.1109/access.2021.3086530 fatcat:hacpqwdxybh63j5ygebqszm7qq

Deep Semantic Segmentation of Natural and Medical Images: A Review [article]

Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
2020 arXiv   pre-print
sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups.  ...  In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics.  ...  microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos.  ... 
arXiv:1910.07655v3 fatcat:uxrrmb3jofcsvnkfkuhfwi62yq

Deep Learning in Medical Image Analysis

Dinggang Shen, Guorong Wu, Heung-Il Suk
2017 Annual Review of Biomedical Engineering  
On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images.  ...  In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease  ...  Specifically, they trained their deep network by using 4,298 axial 2D CT images to learn 5 anatomical classes, i.e., neck, lungs, liver, pelvis, and legs.  ... 
doi:10.1146/annurev-bioeng-071516-044442 pmid:28301734 pmcid:PMC5479722 fatcat:amn6qgpt6fedzp3zejgi4aw66u

Deep Learning in Medical Image Registration: A Review [article]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print
Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.  ...  We summarized the latest developments and applications of DL-based registration methods in the medical field.  ...  Acknowledgements This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718, and Dunwoody Golf Club Prostate Cancer Research  ... 
arXiv:1912.12318v1 fatcat:kuvckosqd5hp7asg6dofhuiis4

Medical Image Registration Using Deep Neural Networks: A Comprehensive Review [article]

Hamid Reza Boveiri, Raouf Khayami, Reza Javidan, Ali Reza MehdiZadeh
2020 arXiv   pre-print
In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented.  ...  On the other hand, the recently huge progress in the field of machine learning made by the possibility of implementing deep neural networks on the contemporary many-core GPUs opened up a promising window  ...  (2018) CNN DIRLAB -4D CT (Lung and Liver) TRE Directly regressing the unimodal deformable 4D CT registration parameters using CNN CREATIS-4D CT (Lung and Liver) Ito and Ino (2018)  ... 
arXiv:2002.03401v1 fatcat:u4utrifr2rg3bf6x6fgohyfmpy

Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks [article]

Rachel Lea Draelos, Lawrence Carin
2021 arXiv   pre-print
Overall, this work advances convolutional neural network explanation approaches and may aid in the development of trustworthy models for sensitive applications.  ...  We illustrate a previously unrecognized limitation of the popular neural network explanation method Grad-CAM: as a side effect of the gradient averaging step, Grad-CAM sometimes highlights locations the  ...  Rubin, MD, FACR, for helpful remarks on the attention maps.  ... 
arXiv:2011.08891v4 fatcat:yo6gawyxdraedbe3nmwnwzvxu4

Artificial Intelligence in Surgery [article]

Xiao-Yun Zhou, Yao Guo, Mali Shen, Guang-Zhong Yang
2019 arXiv   pre-print
Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention.  ...  We end with summarizing the current state, emerging trends and major challenges in the future development of AI in surgery.  ...  [55] proposed a self-supervised depth estimation approach for stereo images using siamese networks. For monocular depth recovery, Mahmood et al.  ... 
arXiv:2001.00627v1 fatcat:dywtv6v36rgf3fummidyluy3zi

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted.  ...  This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images.  ...  Acknowledgments: The authors would thank the Ministry of Higher Education Malaysia And Universiti Sains Malaysia for providing the infrastructures and supports to complete this work.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  We analyse and discuss 163 papers that apply adversarial training techniques in the context of cancer imaging and elaborate their methodologies, advantages and limitations.  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Knowledge-based Radiation Treatment Planning: A Data-driven Method Survey [article]

Shadab Momin, Yabo Fu, Yang Lei, Justin Roper, Jeffrey D. Bradley, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
traditional methods category, whereas deep-learning-based methods included studies that trained neural networks to make dose prediction.  ...  We separated the cited works according to the framework and cancer site in each category.  ...  DL properties such as network architectures (CNN, GAN etc.), training process (supervised, unsupervised, semi-supervised, deep reinforcement etc.), input image types (CT only, CT + OAR + PTV contours,  ... 
arXiv:2009.07388v2 fatcat:rygjxff535dsnhq5gq3ri3fls4
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