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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
2018
2018 24th International Conference on Pattern Recognition (ICPR)
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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
2022
Journal of Imaging
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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
2020
Mathematics
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2021
Multimedia tools and applications
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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]
2021
arXiv
pre-print
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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
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 ...
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
2021
IEEE Access
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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]
2020
arXiv
pre-print
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2020 pre-print arXiv:2004.12150v2 fatcat:j2hrsapa5jejhef2u3lhmedrbeOther Versions
, 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
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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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
2019
Scientific Reports
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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]
2021
arXiv
pre-print
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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
2019
Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19
unpublished
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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
2019
Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19
unpublished
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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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