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Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module

Guanyu Yang, Guoqing Li, Tan Pan, Youyong Kong, Jiasong Wu, Huazhong Shu, Limin Luo, Jean-Louis Dillenseger, Jean-Louis Coatrieux, Lijun Tang, Xiaomei Zhu
2018 2018 24th International Conference on Pattern Recognition (ICPR)  
The method mainly relies on a new threedimensional (3D) fully convolutional network (FCN) which combines the pyramid pooling module (PPM) and gradually enhanced feature module (GEFM).  ...  Accurate kidney and tumor segmentation in CT images is a prerequisite step in the surgery planning. However, automatic kidney and renal tumor segmentation in CT images is still a challenge work.  ...  This research was supported by National Natural Science Foundation under grants (31571001), the Short-Term Recruitment Program of Foreign Experts (WQ20163200398), and Science Foundation for The Excellent  ... 
doi:10.1109/icpr.2018.8545143 dblp:conf/icpr/YangLPKWSLDCTZ18 fatcat:2k3vw3pvljhcbpyga3qsz7cyle

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Abubaker Abdelrahman, Serestina Viriri
2022 Journal of Imaging  
Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images.  ...  Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL.  ...  [54] suggested a 3D fully convolutional network with a pyramid pooling module intended specifically for segmenting kidney and renal pathologies.  ... 
doi:10.3390/jimaging8030055 pmid:35324610 pmcid:PMC8954467 fatcat:7dhh3zwk5zcmpe3ijzbgpmo4ze

Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model

Fuat Türk, Murat Lüy, Necaattin Barışçı
2020 Mathematics  
We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation.  ...  Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance.  ...  Acknowledgments: We would like to thank Tubitak TRUBA for its services in artificial intelligence. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math8101772 fatcat:llqd3k5ulbg25dlydqzbqtc6k4

Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation [article]

Wenshuai Zhao, Dihong Jiang, Jorge Peña Queralta, Tomi Westerlund
2020 arXiv   pre-print
We present a multi-scale supervised 3D U-Net, MSS U-Net, to automatically segment kidneys and kidney tumors from CT images.  ...  Accurate segmentation of kidneys and kidney tumors is an essential step for radiomic analysis as well as developing advanced surgical planning techniques.  ...  In this direction, Yang et al. combined a basic 3D FCN and a pyramid pooling module to enhance the feature extraction capability, with a network able to segment kidneys and kidney tumor at the same time  ... 
arXiv:2004.08108v1 fatcat:r7dljuzrlfbghfscqu5r6r2bhy

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation [article]

Ali Hatamizadeh
2020 arXiv   pre-print
Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines.  ...  In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework  ...  Yang et al. [128] proposed a method for kidney and renal tumor segmentation in CT angiography images by a modified residual FCN that is equipped with a pyramid pooling module.  ... 
arXiv:2006.12706v1 fatcat:6jchhrv6zrhlhbpcak6fcbh4a4

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA.  ...  Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision.  ...  Zhao and Zeng 2019 [190] proposed DLA based on supervised MSS U-Net and 3DU-Net to automatically segment kidneys and kidney tumors from CT images. In the present pandemic situation, Fan et al.  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions [article]

Qian Yu, Lei Qi, Luping Zhou, Lei Wang, Yilong Yin, Yinghuan Shi, Wuzhang Wang, Yang Gao
2021 arXiv   pre-print
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries.  ...  to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets.  ...  H-DenseUNet [15] is a hybrid U-Net model fusing 2D and 3D features for liver tumor segmentation in CT images. Dong et al.  ... 
arXiv:2107.11517v1 fatcat:jtbppo5iprgfbij75b4cxymtgy

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  ...  The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  Segmentation of rectal tumors T2-MRI or CT Novel CNN involving 70 T2-MR and 100 CT on T2-MRI and clinical target cascaded atrous convolution fivefold CV volume segmentation on CT 272 and spatial pyramid  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
application of deep learning models in medical image segmentation.  ...  In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies.  ...  results on brain tumor segmentation from 3D MRI and pancreas tumor segmentation from 3D CT images.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
, lesion and organ segmentation.  ...  Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.  ...  As an extension of the classical CNN, the fully convolutional network (FCN) is a popular pixel-based segmentation network structure [168] .  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

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)  
with Neural Architecture Search DAY 2 -Jan 13, 2021 Ye, Huanran; Liu, Sheng; Jin, Kun; Cheng, Haohao 46 CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote  ...  Segmentation of Axillary and Supraclavicular Tumoral Lymph Nodes in PET/CT: A Hybrid CNN/Component-Tree Approach DAY 4 -Jan 15, 2021 -DAY 4 -Jan 15, 2021 Live Zhao, Zhou; Puybareau, Elodie; Boutry  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

Saleha Masood, Ruogu Fang, Ping Li, Huating Li, Bin Sheng, Akash Mathavan, Xiangning Wang, Po Yang, Qiang Wu, Jing Qin, Weiping Jia
2019 Scientific Reports  
While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector  ...  This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations.  ...  Key Research and Development Program of China (2017YFE0104000, 2016YFC1300302), and the Science and Technology Commission of Shanghai Municipality (18410750700, 17411952600, 16DZ0501100) to B.S.  ... 
doi:10.1038/s41598-019-39795-x pmid:30816296 pmcid:PMC6395677 fatcat:6xtb2j27djerfaxftf2s6cwq7a

A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches [article]

Chen Li, Xintong Li, Md Rahaman, Xiaoyan Li, Hongzan Sun, Hong Zhang, Yong Zhang, Xiaoqi Li, Jian Wu, Yudong Yao, Marcin Grzegorzek
2021 arXiv   pre-print
Since 2004, WSI has been used more and more in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computers, they are highly efficient and labor-saving.  ...  With the development of computer-aided diagnosis (CAD) and image scanning technology, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis.  ...  This structure is based on FCN (Fully Convolutional Neural Network).  ... 
arXiv:2102.10553v1 fatcat:ve4qkiwfjrb3fg7hal5uvpyxia

KiTS challenge: VNet with attention gates and deep supervision

Alzbeta Tureckova, Tomas Turecek, Zuzana Kominkova, Antonio Rodŕıguez-Sánchez
2019 Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19   unpublished
This paper presents the 3D fully convolutional neural network extended by attention gates and deep supervision layers.  ...  The model is able to automatically segment the kidney and kidney-tumor from arterial phase abdominal computed tomography (CT) scans.  ...  For example, recent work by [10] introduces a 3D fully convolutional neural network with pyramid pooling module for kidney-tumor segmentation.  ... 
doi:10.24926/548719.014 fatcat:cwpnkjz6qzcd3gwkfrtm4qm5gu

BiSC-UNet: A fine segmentation framework for kidney and renal tumor

Chuanxia Wang, Yuting He, Xiaoming Qi, Ziteng Zhao, Guanyu Yang, Xiaomei Zhu, Shaobo Zhang, Jean-Louis Dillenseger, Jean-Louis Coatrieux
2019 Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19   unpublished
However, automatic and accurate kidney and renal tumor segmentation in CT images remains a challenge.  ...  Rough SC-UNet is in charge of locating the kidney and renal tumor roughly to achieve the kidney region of interest (ROI) in original CT images.  ...  [11] utilized a three-dimensional (3D) fully convolutional network (FCN) which combines a pyramid pooling module (PPM) for kidney and renal tumor segmentation.  ... 
doi:10.24926/548719.013 fatcat:rw4ptqfl6beibcmbrvhjcij3jy
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