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Deep Learning Initialized and Gradient Enhanced Level-set Based Segmentation for Liver Tumor from CT Images

Yue Zhang, Benxiang Jiang, Jiong Wu, Dongcen Ji, Yilong Liu, Yifan Chen, Ed X. Wu, Xiaoying Tang
2020 IEEE Access  
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

Mubashir Ahmad, Syed Furqan Qadri, M. Usman Ashraf, Khalid Subhi, Salabat Khan, Syeda Shamaila Zareen, Salman Qadri, Rahim Khan
2022 Computational Intelligence and Neuroscience  
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

Christo Ananth, M Kameswari, Densy John Vadakkan, Dr. Niha.K.
2022 Zenodo  
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

Bo Wang, Jingyi Yang, Jingyang Ai, Nana Luo, Lihua An, Haixia Feng, Bo Yang, Zheng You
2021 Frontiers in Medicine  
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

Kan He, Xiaoming Liu, Rahil Shahzad, Robert Reimer, Frank Thiele, Julius Niehoff, Christian Wybranski, Alexander C. Bunck, Huimao Zhang, Michael Perkuhn
2021 Frontiers in Oncology  
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

Mubashir Ahmad, Syed Furqan Qadri, Salman Qadri, Iftikhar Ahmed Saeed, Syeda Shamaila Zareen, Zafar Iqbal, Amerah Alabrah, Hayat Mansoor Alaghbari, Sk. Md. Mizanur Rahman, Vijay Kumar
2022 Computational Intelligence and Neuroscience  
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

Zhou Zheng, Xuechang Zhang, Huafei Xu, Wang Liang, Siming Zheng, Yueding Shi
2018 BioMed Research International  
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

Aihong Yu, Zhe Liu, Victor S. Sheng, Yuqing Song, Xuesheng Liu, Chongya Ma, Wenqiang Wang, Cong Ma
2021 Intelligent Automation and Soft Computing  
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

Mubashir Ahmad, Danni Ai, Guiwang Xie, Syed Furqan Qadri, Hong Song, Yong Huang, Yongtian Wang, Jian Yang
2019 IEEE Access  
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

Hyo Jung Park, Bumwoo Park, Seung Soo Lee
2020 Korean Journal of Radiology  
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]

Dina B. Efremova, Dmitry A. Konovalov, Thanongchai Siriapisith, Worapan Kusakunniran, Peter Haddawy
2019 arXiv   pre-print
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

Marcin Ciecholewski, Michał Kassjański
2021 Sensors  
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]

Yading Yuan
2017 arXiv   pre-print
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

Lei Chen, Hong Song, Chi Wang, Yutao Cui, Jian Yang, Xiaohua Hu, Le Zhang
2019 BMC Bioinformatics  
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]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
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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