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Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions
[article]
2016
arXiv
pre-print
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We propose a novel method, the adaptive local window, for improving level set segmentation technique. ...
We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows. ...
Acknowledgements This project was supported by the National Cancer Institute, National Institutes of Health, under Grants U01CA142555 and R01CA160251. ...
arXiv:1606.03765v1
fatcat:uwg5t2y6fzfftnosgqswgtayvu
Adaptive local window for level set segmentation of CT and MRI liver lesions
2017
Medical Image Analysis
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We propose a novel method, the adaptive local window, for improving level set segmentation technique. ...
We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows. ...
Acknowledgements This project was supported by the National Cancer Institute, National Institutes of Health, under Grants U01CA142555 and R01CA160251. ...
doi:10.1016/j.media.2017.01.002
pmid:28157660
pmcid:PMC5393306
fatcat:h6clgpo6vbfqpozuiqaieer5fm
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis
2017
IEEE Transactions on Medical Imaging
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We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. ...
In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. ...
In addition, we used local window sizes of 5-pixels and 7-pixels surrounding each contour point for MRI and CT liver lesions respectively. ...
doi:10.1109/tmi.2016.2628084
pmid:28113927
pmcid:PMC5510759
fatcat:uxlqhdrwyzdcho4bcyuebliqea
Joint Liver and Hepatic Lesion Segmentation using a Hybrid CNN with Transformer Layers
[article]
2022
arXiv
pre-print
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With Dice similarity scores of averaged 98 +- 2 % for liver and 81 +- 28 % lesion segmentation on the MRI dataset and 97 +- 2 % and 79 +- 25 %, respectively on the CT dataset, the proposed SWTR-Unet outperforms ...
This network was applied to clinical liver MRI, as well as to the publicly available CT data of the liver tumor segmentation (LiTS) challenge. ...
MATERIALS AND METHODS Figure 1 shows the architecture of the proposed SWTR-Unet (SWIN-Transformer-Unet) network for liver and hepatic lesion segmentation in MRI and CT. ...
arXiv:2201.10981v1
fatcat:lucguhusszba7pjs4vzn7ude2e
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. ...
First, we train and apply fully convolutional CNN on CT volumes of the liver for the first time, demonstrating the adaptability to challenging segmentation of hepatic liver lesions. ...
arXiv:1702.05970v2
fatcat:h2rk7soc5nabtf6n24yf3xqgj4
Extrahepatic Disease in Hepatocellular Carcinoma: Do We Always Need Whole-Body CT or Is Liver MRI Sufficient? A Subanalysis of the SORAMIC Trial
2022
Biomedicines
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CT and gadoxetic acid-enhanced liver MRI data sets of 538 HCC patients. ...
To investigate whole-body contrast-enhanced CT and hepatobiliary contrast liver MRI for the detection of extrahepatic disease (EHD) in hepatocellular carcinoma (HCC) and to quantify the impact of EHD on ...
(C): liver MRI, hepatobiliary phase, and axial plane. (D,E): chest CT, lung window, and axial plane. ...
doi:10.3390/biomedicines10051156
pmid:35625900
pmcid:PMC9139039
fatcat:5rfmwqq62zh4njq5n22q7vhu3m
Automatic Liver Segmentation from Abdominal MRI Images using Active Contours
2017
International Journal of Computer Applications
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Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. ...
The proposed segmentation system begins with localizing the liver or a part of it from a given MRI image using biggest components analysis. ...
The rest of this paper is organized as follows: Section 2 presents a literature survey for liver segmentation from MRI and CT images. ...
doi:10.5120/ijca2017915512
fatcat:yca6xkl5vjgs3cpcmilyyxfw2e
Batch Normalized Convolution Neural Network for Liver Segmentation
2020
Zenodo
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Models for the preoperative, for example, planning to understand the complex internal structure of the liver and precisely localize the liver surface and its tumors; there are various algorithms proposed ...
The proposed liver segmentation model consists of four main steps: pre-processing, training the BATA-Convnet, liver segmentation, and the postprocessing step to maximize the result efficiency. ...
Christ et al. ware presented a combined automatic segmentation of the liver and its lesions in CT and MRI abdomen images using two cascaded fully convolutional neural networks (CFCNs) one for the segmentation ...
doi:10.5281/zenodo.4264377
fatcat:mm4uzuxkqfcxdp3i4liwwu64pq
Protocol requirements and diagnostic value of PET/MR imaging for liver metastasis detection
2013
European Journal of Nuclear Medicine and Molecular Imaging
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Purpose To compare the accuracy of PET/MR imaging with that of FDG PET/CT and to determine the MR sequences necessary for the detection of liver metastasis using a trimodality PET/CT/MR set-up. ...
Imaging using a trimodality PET/CT/MR set-up (time-of-flight PET/CT and 3-T whole-body MR imager) comprised PET, low-dose CT, contrast-enhanced (CE) CT of the abdomen, and MR with T1-W/T2-W, diffusion-weighted ...
All CT images of the abdomen were reconstructed using adaptive iterative reconstruction with a soft-tissue convolution kernel at a standardized window setting (window width 350 HU, window level 50 HU). ...
doi:10.1007/s00259-013-2654-x
pmid:24346415
fatcat:fydjn4kjf5f2heubse2oa4whha
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
[article]
2020
arXiv
pre-print
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CT and MRI, as well as cardiac segmentation for MRI. ...
imbalance problem in medical image segmentation; (3) We demonstrate the general applicability of the proposed approach for medical images using three different tasks: abdominal organ segmentation for ...
The authors would like to thank Konstantinos Kamnitsas and Zeju Li for insightful comments. ...
arXiv:2007.09886v2
fatcat:tnmhky4sn5cv5ojlvglmgvcti4
Deep Learning Model With Convolutional Neural Network for Detecting and Segmenting Hepatocellular Carcinoma in CT: A Preliminary Study
2022
Cureus
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The sensitivity for identifying HCC of the model and Dice score for segmentation of liver masses between radiologists and the CNN model were calculated for the test set. ...
Results The sensitivity for HCC identification of the model was 100%. The median Dice score for HCC segmenting between radiologists and the CNN model was 0.81 for the test set. ...
For liver lesions, several studies have used CT and MRI images to develop CNN models, and their positive results have shown that artificial intelligence, particularly those based on CNN methods, could ...
doi:10.7759/cureus.21347
pmid:35186603
pmcid:PMC8849436
fatcat:j6h35enwd5hlbenaekyuyhzbgy
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation
[article]
2018
arXiv
pre-print
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for the segmentation of the liver from CT scans. ...
volumetric Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) medical image segmentations inside the AR headset, that does not need human intervention for loading, processing and segmentation ...
The radiologists perform a task named "windowing and leveling" which means they set the window "level" and window "width". An example is depicted in Figure 2 .3. ...
arXiv:1808.04929v1
fatcat:ruvry5jja5bfpbmjbez2dyvx5a
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
2020
Frontiers in Bioengineering and Biotechnology
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Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. ...
However, few studies investigate 3D networks for liver tumor segmentation. ...
ZM, CS, and HC participated in manuscript writing. RS designed the experiments and edited the manuscript. All authors contributed to the article and approved the submitted version. ...
doi:10.3389/fbioe.2020.605132
pmid:33425871
pmcid:PMC7785874
fatcat:kp6ob7jtpfbqdab73kiju3w7hy
Companding algorithm for the detection of malignant lesions in HDR CT mandibular images
[article]
2020
arXiv
pre-print
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Besides the general need for simplification of the window setting method for the purpose of diagnosis, there are specific clinical needs for the resection of malignant lesions in the mandible, for example ...
Each channel contains different set of parameters. The AMCC algorithm successfully and adaptively compands a large variety of mandibular CT HDR images as well as natural images. ...
The window technique is also required and used for medical care and diagnosis of lesions in the mandible. ...
arXiv:2003.13588v1
fatcat:vknf7pxphzcchpacqrykj5rmey
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
Open challenges and directions for future research are discussed. ...
ArXiv was searched for papers mentioning one of a set of terms related to medical imaging. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
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