Model-Guided Segmentation of Liver in CT and PET-CT Images of Child Patients Based on Statistical Region Merging

2016 ◽  
Author(s):  
Jiri Sedlar ◽  
Mariusz Bajger ◽  
Martin Caon ◽  
Gobert Lee
Keyword(s):  
Ct Images ◽  
Region Merging ◽  
Pet Ct ◽  
Child Patients ◽  
Statistical Region Merging

scholarly journals AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

2017 ◽  
Vol 36 (2) ◽  
pp. 65 ◽  
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Lung Nodule ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Region Merging ◽  
Three Dimensional Objects ◽  
Ct Data ◽  
Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

scholarly journals Automatic Pulmonary Nodule Growth Measurement through CT Image Analysis based on Morphology Filtering and Statistical Region Merging

2018 ◽  
Vol 11 (3) ◽  
pp. 1247-1259
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Treatment Session ◽  
Support Vector ◽  
Svm Classifier ◽  
Ct Image ◽  
Region Merging ◽  
Growth Measurement ◽  
Statistical Region Merging

This paper proposes an innovative method for automatic detection of pulmonary nodules in Computed Tomography (CT) data and measurement of changes in the number and sizes of the detected nodules during the treatment session. In the presented method, two multi-slice CT images are first taken from the patient’s lung, each captured by a similar capturing device but at two different dates. The CT images are then analyzed and their pulmonary nodules are extracted using a novel framework based on Mathematical Morphology Filtering (MMF), Statistical Region Merging (SRM), and Support Vector Machines (SVM). The MMF step smoothes the image in order to increase its homogeneity as well as removing the noises and artifacts. The SRM algorithm segments each slice of the CT image. After connecting the boundaries of the segments in adjacent slices, three-dimensional objects are produced which are considered as nodule-candidates. These candidates are classified into nodules and non-nodules using a two-class SVM classifier. The extracted nodules in each image are then labeled and their characteristics (i.e. labels, locations, and sizes) are stored. Finally, after registering the image pair using an affine algorithm, the growth rates of the lung nodules are measured.

scholarly journals Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

2013 ◽  
Vol 54 (10) ◽  
pp. 1703-1709 ◽  
Author(s):  
N.-M. Cheng ◽  
Y.-H. Dean Fang ◽  
J. Tung-Chieh Chang ◽  
C.-G. Huang ◽  
D.-L. Tsan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Fdg Pet ◽  
Prognostic Significance ◽  
Ct Images ◽  
Textural Features ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

[68Ga]Ga-FAPI and [18F]FDG PET/CT images in a patient with juvenile polymyositis

2021 ◽  
Author(s):  
Jieling Zheng ◽  
Huaning Chen ◽  
Kaixian Lin ◽  
Shaobo Yao ◽  
Weibing Miao
Keyword(s):  
Fdg Pet ◽  
Ct Images ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Increased [18F]FMISO accumulation under hypoxia by multidrug-resistant protein 1 inhibitors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoichi Shimizu ◽  
Yukihiro Nakai ◽  
Hiroyuki Watanabe ◽  
Shimpei Iikuni ◽  
Masahiro Ono ◽  
...  
Keyword(s):  
Cyclosporine A ◽  
Pet Imaging ◽  
Multidrug Resistant ◽  
Ct Images ◽  
Pet Scan ◽  
Reduced Form ◽  
Pet Ct ◽  
Fadu Cells ◽  
Hypoxic State

Abstract Background [18F]Fluoromisonidazole ([18F]FMISO) is a PET imaging probe widely used for the detection of hypoxia. We previously reported that [18F]FMISO is metabolized to the glutathione conjugate of the reduced form in hypoxic cells. In addition, we found that the [18F]FMISO uptake level varied depending on the cellular glutathione conjugation and excretion ability such as enzyme activity of glutathione-S-transferase and expression levels of multidrug resistance-associated protein 1 (MRP1, an efflux transporter), in addition to the cellular hypoxic state. In this study, we evaluated whether MRP1 activity affected [18F]FMISO PET imaging. Methods FaDu human pharyngeal squamous cell carcinoma cells were pretreated with MRP1 inhibitors (cyclosporine A, lapatinib, or MK-571) for 1 h, incubated with [18F]FMISO for 4 h under hypoxia, and their radioactivity was then measured. FaDu tumor-bearing mice were intravenously injected with [18F]FMISO, and PET/CT images were acquired at 4 h post-injection (1st PET scan). Two days later, the same mice were pretreated with MRP1 inhibitors (cyclosporine A, lapatinib, or MK-571) for 1 h, and PET/CT images were acquired (2nd PET scan). Results FaDu cells pretreated with MRP1 inhibitors exhibited significantly higher radioactivity than those without inhibitor treatment (cyclosporine A: 6.91 ± 0.27, lapatinib: 10.03 ± 0.47, MK-571: 10.15 ± 0.44%dose/mg protein, p < 0.01). In the in vivo PET study, the SUVmean ratio in tumors [calculated as after treatment (2nd PET scan)/before treatment of MRP1 inhibitors (1st PET scan)] of the mice treated with MRP1 inhibitors was significantly higher than those of control mice (cyclosporine A: 2.6 ± 0.7, lapatinib: 2.2 ± 0.7, MK-571: 2.2 ± 0.7, control: 1.2 ± 0.2, p < 0.05). Conclusion In this study, we revealed that MRP1 inhibitors increase [18F]FMISO accumulation in hypoxic cells. This suggests that [18F]FMISO-PET imaging is affected by MRP1 inhibitors independent of the hypoxic state.

scholarly journals Impact of the Q.Clear reconstruction algorithm on the interpretation of PET/CT images in patients with lymphoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michał Wyrzykowski ◽  
Natalia Siminiak ◽  
Maciej Kaźmierczak ◽  
Marek Ruchała ◽  
Rafał Czepczyński
Keyword(s):  
Reconstruction Algorithm ◽  
Ct Images ◽  
Pet Ct

scholarly journals Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Amy J. Weisman ◽  
Jihyun Kim ◽  
Inki Lee ◽  
Kathleen M. McCarten ◽  
Sandy Kessel ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Correlation Coefficient ◽  
Pet Imaging ◽  
Fdg Pet ◽  
Relative Difference ◽  
Ct Images ◽  
Imaging Features ◽  
Pediatric Hodgkin Lymphoma ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Purpose For pediatric lymphoma, quantitative FDG PET/CT imaging features such as metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies. However, feature extraction is difficult and time-consuming in cases of high disease burden. The purpose of this study was to fully automate the measurement of PET imaging features in PET/CT images of pediatric lymphoma. Methods 18F-FDG PET/CT baseline images of 100 pediatric Hodgkin lymphoma patients were retrospectively analyzed. Two nuclear medicine physicians identified and segmented FDG avid disease using PET thresholding methods. Both PET and CT images were used as inputs to a three-dimensional patch-based, multi-resolution pathway convolutional neural network architecture, DeepMedic. The model was trained to replicate physician segmentations using an ensemble of three networks trained with 5-fold cross-validation. The maximum SUV (SUVmax), MTV, total lesion glycolysis (TLG), surface-area-to-volume ratio (SA/MTV), and a measure of disease spread (Dmaxpatient) were extracted from the model output. Pearson’s correlation coefficient and relative percent differences were calculated between automated and physician-extracted features. Results Median Dice similarity coefficient of patient contours between automated and physician contours was 0.86 (IQR 0.78–0.91). Automated SUVmax values matched exactly the physician determined values in 81/100 cases, with Pearson’s correlation coefficient (R) of 0.95. Automated MTV was strongly correlated with physician MTV (R = 0.88), though it was slightly underestimated with a median (IQR) relative difference of − 4.3% (− 10.0–5.7%). Agreement of TLG was excellent (R = 0.94), with median (IQR) relative difference of − 0.4% (− 5.2–7.0%). Median relative percent differences were 6.8% (R = 0.91; IQR 1.6–4.3%) for SA/MTV, and 4.5% (R = 0.51; IQR − 7.5–40.9%) for Dmaxpatient, which was the most difficult feature to quantify automatically. Conclusions An automated method using an ensemble of multi-resolution pathway 3D CNNs was able to quantify PET imaging features of lymphoma on baseline FDG PET/CT images with excellent agreement to reference physician PET segmentation. Automated methods with faster throughput for PET quantitation, such as MTV and TLG, show promise in more accessible clinical and research applications.

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