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Deep Learning Initialized and Gradient Enhanced Level-set Based Segmentation for Liver Tumor from CT Images
2020
IEEE Access
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In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. ...
At the preprocessing step, the CT image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding liver and liver tumor. ...
Zhang from the Jiangsu Province Hospital for making the ISICDM dataset for this research available. ...
doi:10.1109/access.2020.2988647
fatcat:n6hdm2mptje7bbx3aphlmuss2e
Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning
2022
Computational Intelligence and Neuroscience
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In this paper, we propose a patch-based deep learning method for the segmentation of a liver from CT images using SAE. ...
Segmentation of a liver in computed tomography (CT) images is an important step toward quantitative biomarkers for a computer-aided decision support system and precise medical diagnosis. ...
for feature learning, and test images are sent to the trained model to segment the liver from CT images. ...
doi:10.1155/2022/2665283
pmid:35634046
pmcid:PMC9132625
fatcat:n4uqij6sf5hklcddv4owx7wjyy
Enhancing Segmentation Approaches from Fuzzy-MPSO Based Liver Tumor Segmentation to Gaussian Mixture Model and Expected Maximization
2022
Zenodo
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The Gaussian mixture model (GMM) and Expected Maximization for liver tumor division are introduced. In the early liver division process Level set models are utilized. ...
Christo Ananth et al. discussed that Liver tumor division in restorative pictures has been generally considered as of late, of which the Level set models show an uncommon potential with the advantage of ...
Accurate and reliable segmentation of liver tissue and tumors is necessary for CT-based hepatic diagnosis [3] . ...
doi:10.5281/zenodo.6791198
fatcat:xuox37wkgbetxjmchvy2t3eodu
Accurate Tumor Segmentation via Octave Convolution Neural Network
2021
Frontiers in Medicine
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Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. ...
In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. ...
Therefore, in this paper, a deep learning method based on learning and decoding layered features with multiple spatial frequencies is proposed to achieve 3D liver tumor segmentation from CT images. ...
doi:10.3389/fmed.2021.653913
pmid:34095168
pmcid:PMC8169966
fatcat:bqlgbbnhqvc3blskhgrzha62mm
Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT
2021
Frontiers in Oncology
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Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. ...
The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT ...
However, due to the heterogeneous and diffusive liver and tumor shapes, segmenting them from the CT images is quite challenging. ...
doi:10.3389/fonc.2021.669437
pmid:34336661
pmcid:PMC8320434
fatcat:tis2wuvvxnandgwa7eq3p7f3t4
A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis
2022
Computational Intelligence and Neuroscience
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Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. ...
Currently, many deep learning methods are used for liver segmentation that takes a long time to train the model which makes this task challenging and limited to larger hardware resources. ...
Deep learning methods are being used to detect the tumor from CT images and get reasonable results [56, 57] . ...
doi:10.1155/2022/7954333
pmid:35755754
pmcid:PMC9225858
fatcat:v72zcetexzc47bd4q4c72bpuku
A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images
2018
BioMed Research International
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This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. ...
Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. ...
LY17E050011 and the research project on key technologies of complex surgery for liver resection based on 3D printing that was funded by Ningbo, China, under Grant no. 2015C50025. ...
doi:10.1155/2018/3815346
pmid:30159326
fatcat:l6sfgh6fmzczlmtm4hmvmudpli
CT Segmentation of Liver and Tumors Fused Multi-Scale Features
2021
Intelligent Automation and Soft Computing
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To deal with these problems, we proposed effective methods for enhancing features and processed public datasets from Liver Tumor Segmentation Challenge (LITS) for the verification. ...
In this experiment, data pre-processing based on the image enhancement and noise reduction. ...
Acknowledgement: The authors would like to thank Radiologists of the Medical Imaging department of Affiliated Hospital of Jiangsu University, University of Central Arkansas, the First Affiliated Hospital ...
doi:10.32604/iasc.2021.019513
fatcat:tsazi3v6jbchjdv4n334r2fhqu
Deep Belief Network Modelling for Automatic Liver Segmentation
2019
IEEE Access
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In this paper, we propose an automatic feature learning algorithm based on the deep belief network (DBN) for liver segmentation. ...
The liver segmentation in CT scan images is a significant step toward the development of a quantitative biomarker for computer-aided diagnosis. ...
A study based on a deep belief network (DBN) is proposed in the literature for unsupervised feature learning, and the network was fine tuned to segment vertebrae from CT images [25] , [43] . ...
doi:10.1109/access.2019.2896961
fatcat:ifb4j6udjnar7jjhpoi5fwiyxm
Radiomics and Deep Learning: Hepatic Applications
2020
Korean Journal of Radiology
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hepatic tumors, and segmenting the liver and liver tumors. ...
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. ...
(61) have reported the use of a deep learning algorithm for the automatic detection and segmentation of malignant liver tumors on CT images. ...
doi:10.3348/kjr.2019.0752
pmid:32193887
pmcid:PMC7082656
fatcat:kncq2om26rg5hf4quusf6z5wk4
Automatic segmentation of kidney and liver tumors in CT images
[article]
2019
arXiv
pre-print
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Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. ...
The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores. ...
At the moment a large number of solutions have been proposed for liver tumor segmentation from CT images. ...
arXiv:1908.01279v2
fatcat:i5huxcjiajepvjzepm257ozm6y
Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review
2021
Sensors
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This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. ...
This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). ...
) and (liver vessel segmentation deep learning) and (CT or CTA or MR or USG)). ...
doi:10.3390/s21062027
pmid:33809361
fatcat:dowsqsuwlrgyjic7ozoa7mr23q
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
[article]
2017
arXiv
pre-print
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MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation ...
Automatic segmentation of liver and its tumors is an essential step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and assessment of tumor response to ...
The initial learning rate was set as 0.003. ...
arXiv:1710.04540v1
fatcat:zppycxvqerguhcs3y2b3vk6ne4
Liver tumor segmentation in CT volumes using an adversarial densely connected network
2019
BMC Bioinformatics
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Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 ...
In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography ...
Acknowledgements We would like to thank the NiftyNet team for providing an open-source platform which can build new solutions of our own problems.
About this supplement ...
doi:10.1186/s12859-019-3069-x
pmid:31787071
pmcid:PMC6886252
fatcat:2pfk7enaerdpbo65vgrsbosutq
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Head 673 Deep Recurrent Level Set for Segmenting Brain Tumors 676 Deep convolutional filtering for spatio-temporal denoising ...
from Undersampled k-space using Deep Latent Representation Learning 277 Towards radiotherapy enhancement and real time tumor radiation dosimetry through 3D imaging of gold nanoparticles using XFCT 279 ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
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