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Modified Density-Based Data Clustering for Interactive Liver Segmentation
2018
Journal of Image and Graphics
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Identifying the liver region from abdominal Computed Tomography (CT) scans is still a challenging task due to the complexity of the liver's anatomy, similar intensity with adjacent organs and presence ...
The experimental results show that the proposed system is effective for accurate detection of the liver surface in comparison with other related works in the literature. . ...
INTRODUCTION Liver segmentation from computerized tomography (CT) scans is a crucial and primary step for computeraided liver disease diagnosis and surgical planning. ...
doi:10.18178/joig.6.1.84-87
fatcat:tkj6cwz2indxpdwo6gv2l74bhy
Computer Aided Preoperative Evaluation of the Residual Liver Volume Using Computed Tomography Images
2014
Journal of digital imaging
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For this purpose, a technique for evaluation of liver volume from computed tomography (CT) images has been developed. ...
Furthermore, the methodology algorithms were implemented and incorporated within a software tool with three basic functionalities: volume determination based on segmentation of liver from CT images, virtual ...
Initially, we developed a methodology for evaluation of the liver volume from CT images. ...
doi:10.1007/s10278-014-9737-5
pmid:25273505
pmcid:PMC4359196
fatcat:quhagvegynhq7ivgqjwyq2jblq
Automatic segmentation of kidney and liver tumors in CT images
[article]
2019
arXiv
pre-print
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We have chosen MICCAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. ...
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. ...
Table 1 . 1 Comparison of liver and liver tumor segmentation results on 3DIRCAB dataset. ...
arXiv:1908.01279v2
fatcat:i5huxcjiajepvjzepm257ozm6y
Overview of Technical Elements of Liver Segmentation
2016
International Journal of Advanced Computer Science and Applications
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The aim of this paper is to assemble a wide assortment of techniques and used CT scan dataset information for liver segmentation that will provide a decent beginning to the new researcher. ...
Liver diseases are life-threatening, it's important to detect it tumor in early stages. So, for tumor detection Segmentation of the liver is a first and significant stride. ...
The dataset is very small. [12] Proposed a novel system for automatically detecting and segmenting focal liver region from CT images. ...
doi:10.14569/ijacsa.2016.071235
fatcat:vontjjcbxjbpxiclrgse4ve6fi
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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Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. ...
In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. ...
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
Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images
2020
Korean Journal of Radiology
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A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. ...
Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver ...
Acknowledgments Seung Soo Lee was responsible for the clinical study, and Heung-Il Suk was responsible for the development of the deep learning algorithm. ...
doi:10.3348/kjr.2020.0237
pmid:32677383
pmcid:PMC7369202
fatcat:ort6lvdtazcjtoy23sfqb5heeu
An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images
2018
Journal of Healthcare Engineering
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The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. ...
It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images. ...
Figure 13 : 13 Figure13: Evaluation of the segmentation performance of the improved fuzzy connectedness method on 10 cases randomly selected from the 3Dircadb dataset for the values of T ranging from 0.01 ...
doi:10.1155/2018/2376317
fatcat:dnwrffhrnfaajehjmydjpu5mmy
Bata-Unet: Deep Learning Model for Liver Segmentation
2020
Signal & Image Processing An International Journal
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Classical Based method for Segmentation. ...
The proposed method was able to achieve highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for 3D-IRCAD dataset =0.96%. ...
The proposed modified U-Net (mU-Net) was evaluated using two public datasets which are Liver tumor segmentation (LiTS) challenge 2017 and 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb ...
doi:10.5121/sipij.2020.11505
fatcat:jrr2hzn47bbq7hksnmq3jhk47y
Liver Segmentation from Multimodal Images using HED-Mask R-CNN
[article]
2019
arXiv
pre-print
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Precise segmentation of the liver is critical for computer-aided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. ...
We significantly improved the segmentation accuracy of CT and MRI images on a database with a Dice value of 0.94 for CT, 0.89 for T2-weighted MRI and 0.91 for T1-weighted MRI. ...
Table 1 . 1 Comparison of liver segmentation on CT and MRI scans Quantitative comparison among Mask R-CNN and HED-Mask R-CNN
CT Segmentation
Dice
MRI Segmentation
Dice
Mask R-CNN
(N=491)
0.90 ...
arXiv:1910.10504v1
fatcat:m5vcnitijbd3llz6cshzg2kwue
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
[article]
2017
arXiv
pre-print
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This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale ...
We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. ...
Dataset The second dataset we evaluated is a real-life clinical CT dataset from multiple CT scanners and acquired at different centers. It compromises 100 CT scans from different patients. ...
arXiv:1702.05970v2
fatcat:h2rk7soc5nabtf6n24yf3xqgj4
Towards automated planning for unsealed source therapy
2012
Journal of Applied Clinical Medical Physics
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Automatic segmentation showed good agreement with manual contouring, measured using the dice similarity coefficient and ranging from 0.72 to 0.87 for the liver, 0.47 to 0.93 for the kidneys, and 0.74 to ...
To overcome the lack of common anatomical features between the CT and SPECT datasets, registration is achieved through a narrow band approach that matches liver contours in the CT with the gradients of ...
(10) that matches liver segmentations obtained from SPECT and CT datasets. ...
doi:10.1120/jacmp.v13i4.3789
pmid:22766948
pmcid:PMC5716513
fatcat:uywdh6omcnh2ph7u6a73mun33u
Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
[article]
2017
arXiv
pre-print
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The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer. ...
The method was tested on 3Dircadb and LITS dataset. ...
Conclusion We have presented a novel automated and unsupervised method for segmentation of lesions in liver CT scans. ...
arXiv:1704.02348v1
fatcat:elioon4q6rgqjpcl7lfcdte42m
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. ...
Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. ...
Zhang from the Jiangsu Province Hospital for making the ISICDM dataset for this research available. ...
doi:10.1109/access.2020.2988647
fatcat:n6hdm2mptje7bbx3aphlmuss2e
Multi-Stage Liver Segmentation in CT Scans Using Gaussian Pseudo Variance Level Set
2021
IEEE Access
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More specifically, the proposed method is evaluated on 40 CT scan images, which are publicly available on three databases: SLIVER07, 3Dircadb, and LiTS. ...
This paper presents a Multi-stage framework for location and segmentation. First, Faster RCNN is employed to locate the liver region. ...
COMPARISON WITH STATE-OF-THE-ART METHODS We compare our method with recently published methods based on SLIVER07, 3Dircadb, and LiTS to evaluate the performance of the proposed liver segmentation framework ...
doi:10.1109/access.2021.3097387
fatcat:227bycuhpzhr7dybezs3jbkyq4
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
[chapter]
2016
Lecture Notes in Computer Science
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2016 pre-print arXiv:1610.02177v1 fatcat:vu4rxtncmvgtxpxm276pzw33n4
The second FCN solely segments lesions from the predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a dense 3D CRF that accounts for both spatial coherence and appearance. ...
We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. ...
In our particular study, we use L = {0, 1, 2} for background, liver and lesion, respectively.
3DIRCADb Dataset For clinical routine usage, methods and algorithms have to be developed, trained and evaluated ...
doi:10.1007/978-3-319-46723-8_48
fatcat:wavw5gabtrfgnbwsjxlj7khati
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