scholarly journals Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Virginia Liberini ◽  
Bruno De Santi ◽  
Osvaldo Rampado ◽  
Elena Gallio ◽  
Beatrice Dionisi ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Ct Images ◽  
Mean Value ◽  
Biological Information ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation ◽  
Pet Ct ◽  
Segmentation Methods ◽  
Edge Based ◽  
The Impact

Abstract Objective To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. Methods Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs’ correlation with volume and SUVmax was analyzed by calculating Pearson’s correlation coefficients. Results DSC mean value was 0.75 ± 0.11 (0.45–0.92) between SAEB and operators and 0.78 ± 0.09 (0.36–0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax. Conclusions RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals U-Net combined with multi-scale attention mechanism for liver segmentation in CT images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiawei Wu ◽  
Shengqiang Zhou ◽  
Songlin Zuo ◽  
Yiyin Chen ◽  
Weiqin Sun ◽  
...  
Keyword(s):  
Work Experience ◽  
Spatial Location ◽  
Ct Images ◽  
Attention Mechanism ◽  
Dice Similarity Coefficient ◽  
Liver Segmentation ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Spatial Location Information ◽  
Liver Region

Abstract Background The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).

A unified framework for simultaneous assessment of accuracy, between-, and within-reader variability of image segmentations

2020 ◽  
Vol 29 (11) ◽  
pp. 3135-3152
Author(s):  
Andrew E Ghattas ◽  
Reinhard R Beichel ◽  
Brian J Smith
Keyword(s):  
Manual Segmentation ◽  
Unified Framework ◽  
Modeling Framework ◽  
Pet Ct ◽  
Segmentation Methods ◽  
Wide Range ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Ct Data ◽  
Method Accuracy

Medical imaging is utilized in a wide range of clinical applications. To enable a detailed quantitative analysis, medical images must often be segmented to label (delineate) structures of interest; for example, a tumor. Frequently, manual segmentation is utilized in clinical practice (e.g., in radiation oncology) to define such structures of interest. However, it can be quite time consuming and subject to substantial between-, and within-reader variability. A more reproducible, less variable, and more time efficient segmentation approach is likely to improve medical treatment. This potential has spurred the development of segmentation algorithms which harness computational power. Segmentation algorithms’ widespread use is limited due to difficulty in quantifying their performance relative to manual segmentation, which itself is subject to variation. This paper presents a statistical model which simultaneously estimates segmentation method accuracy, and between- and within-reader variability. The model is simultaneously fit for multiple segmentation methods within a unified Bayesian framework. The Bayesian model is compared to other methods used in literature via a simulation study, and application to head and neck cancer PET/CT data. The modeling framework is flexible and can be employed in numerous comparison applications. Several alternate applications are discussed in the paper.

scholarly journals Clinically feasible semi-automatic workflows for measuring metabolically active tumour volume in metastatic melanoma

2020 ◽  
Author(s):  
Joyce van Sluis ◽  
Ellen C. de Heer ◽  
Mayke Boellaard ◽  
Mathilde Jalving ◽  
Adrienne H. Brouwers ◽  
...  
Keyword(s):  
Overall Survival ◽  
Metastatic Melanoma ◽  
Pet Imaging ◽  
Fdg Pet ◽  
Tumour Volume ◽  
Imaging Biomarker ◽  
Manual Segmentation ◽  
Pet Ct ◽  
Segmentation Methods ◽  
Automatic Methods

Abstract Purpose Metabolically active tumour volume (MATV) is a potential quantitative positron emission tomography (PET) imaging biomarker in melanoma. Accumulating data indicate that low MATV may predict increased chance of response to immunotherapy and overall survival. However, metastatic melanoma can present with numerous (small) tumour lesions, making manual tumour segmentation time-consuming. The aim of this study was to evaluate multiple semi-automatic segmentation workflows to determine reliability and reproducibility of MATV measurements in patients with metastatic melanoma. Methods An existing cohort of 64 adult patients with histologically proven metastatic melanoma was used in this study. 18F-FDG PET/CT diagnostic baseline images were acquired using a European Association of Nuclear Medicine (EANM) Research Limited–accredited Siemens Biograph mCT PET/CT system (Siemens Healthineers, Knoxville, USA). PET data were analysed using manual, gradient-based segmentation and five different semi-automatic methods: three direct PET image–derived delineations (41MAX, A50P and SUV40) and two based on a majority-vote approach (MV2 and MV3), without and with (suffix ‘+’) manual lesion addition. Correlation between the different segmentation methods and their respective associations with overall survival was assessed. Results Correlation between the MATVs derived by the manual segmentation and semi-automated tumour segmentations ranged from R2 = 0.41 for A50P to R2 = 0.85 for SUV40+ and MV2+, respectively. Manual MATV segmentation did not differ significantly from the semi-automatic methods SUV40 (∆MATV mean ± SD 0.08 ± 0.60 mL, P = 0.303), SUV40+ (∆MATV − 0.10 ± 0.51 mL, P = 0.126), MV2+ (∆MATV − 0.09 ± 0.62 mL, P = 0.252) and MV3+ (∆MATV − 0.03 ± 0.55 mL, P = 0.615). Log-rank tests showed statistically significant overall survival differences between above and below median MATV patients for all segmentation methods with areas under the ROC curves of 0.806 for manual segmentation and between 0.756 [41MAX] and 0.807 [MV3+] for semi-automatic segmentations. Conclusions Simple and fast semi-automated FDG PET segmentation workflows yield accurate and reproducible MATV measurements that correlate well with manual segmentation in metastatic melanoma. The most readily applicable and user-friendly SUV40 method allows feasible MATV measurement in prospective multicentre studies required for validation of this potential PET imaging biomarker for clinical use.

scholarly journals Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review

2018 ◽  
Vol 7 (3.32) ◽  
pp. 137
Author(s):  
Farli Rossi ◽  
Ashrani Aizzuddin Abd Rahni
Keyword(s):  
Image Segmentation ◽  
Ct Images ◽  
Basic Knowledge ◽  
Low Contrast ◽  
Tumor Delineation ◽  
Innovative Methods ◽  
Pet Ct ◽  
Segmentation Methods ◽  
Disease Staging ◽  
Puzzle Task

Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation of tumor in PET - CT images is a well-known puzzle task. This paper attempted to describe and review four innovative methods used in the joint segmentation of functional and anatomical PET - CT images for tumor delineation. For the basic knowledge, the state of the art image segmentation methods were briefly reviewed and fundamental of PET and CT images were briefly explained. Further, the specific characteristics and limitations of four joint segmentation methods were critically discussed.  

scholarly journals The impact of fatigue on agility and responsiveness in boxing

2019 ◽  
Vol 11 (1) ◽  
pp. 131-135
Author(s):  
Edyta Sienkiewicz-Dianzenza ◽  
Łukasz Maszczyk
Keyword(s):  
Performance Index ◽  
Correlation Coefficients ◽  
Mean Value ◽  
Training Activities ◽  
Student’S T ◽  
External Stimuli ◽  
The Impact ◽  
Reaction Speed ◽  
Shuttle Run ◽  
Student’S T Test

SummaryStudy aim: To assess the effects of fatigue on agility and responsiveness in boxing.Material and methods: Agroup of 20 amateur boxers aged 14–45 years participated in the study. Ditrich’s test and acomputer test, both measuring the speed of reaction to avisual stimulus, as well as agility run and 4 × 10 m shuttle run with carrying blocks, both measuring agility, were performed. Running agility and reaction speed were measured at 3levels of fatigue expressed by the heart rates. The capacity to maintain the highest possible level of measured variables was assessed by applying the performance index (PI) (mean value of three or four (in the case of Ditrich’s test) repetitions to the maximum one). Student’s t-test for dependent data and Pearson’s correlation coefficients were used in data analysis, the level p ≤ 0.05 being considered significant.Results: Both running agility and responsiveness markedly decreased with mounting fatigue, e.g. running speed from 1.73 ± 0.12 m/s to 1.55 ± 0.11 m/s.Conclusion: Developing anaerobic endurance would markedly improve agility skills and speed of reaction to external stimuli. Measuring the performance index (PI) from short, maximal, repeated exertions spaced with constant intermissions may be a valuable tool in directing training activities towards development of selected elements of boxers’ physical fitness.

scholarly journals Dosimetric Impact of Deep Learning-Based CT Auto-Segmentation on IMRT Treatment Planning for Prostate Cancer

2021 ◽  
Author(s):  
Maria Kawula ◽  
Dinu Purice ◽  
Minglun Li ◽  
Gerome Vivar ◽  
Seyed-Ahmad Ahmadi ◽  
...  
Keyword(s):  
State Of The Art ◽  
Pearson Correlation ◽  
Automatic Segmentation ◽  
Volumetric Modulated Arc Therapy ◽  
Target Volume ◽  
Ct Images ◽  
Pass Rate ◽  
Dice Similarity Coefficient ◽  
Dose Distributions ◽  
The Impact

Abstract Background The evaluation of the automatic segmentation algorithms is commonly performed using geometric metrics, yet an evaluation based on dosimetric parameters might be more relevant in clinical practice but is still lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in intensity-modulated radiation therapy (IMRT) for prostate patients for the first time. Methods A database of 69 computed tomography (CT) images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, gamma index and DVH parameters, has been calculated. Results 3D U-Net based segmentation achieved a DSC of 0.87(0.03) for prostate, 0.97(0.01) for bladder and 0.89(0.04) for rectum. The mean and 95% HD were below 1.6(0.4) and below 5(4) mm, respectively. The DVH parameters V 60/65/70 Gy for the bladder and V 50/65/70 Gy for the rectum showed agreement between dose distributions within ±5% and ±2%, respectively. The DVH parameters for prostate and prostate+3mm margin (surrogate clinical target volume) showed good target coverage for the 3D U-Net segmentation with the exception of one case. The average gamma pass-rate was 85\%. A comparison between geometric and dosimetric metrics showed no strong statistically significant correlation between these metrics. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. The study highlighted the importance of dosimetric evaluation on top of standard geometric parameters and concluded that the automatic segmentation is sufficiently accurate to assist the physicians in manually contouring organs in CT images of the male pelvic region, which is an important step towards a fully automated workflow in IMRT.

PD-0293 REPRODUCIBILITY AND VOLUME SEGMENTATION METHODS ON F-MISO AND FLT PET-CT IMAGES IN PATIENTS WITH LUNG CANCER

2012 ◽  
Vol 103 ◽  
pp. S115
Author(s):  
S. Thureau ◽  
P. Chaumet-Riffaud ◽  
P. Fernandez ◽  
B. Bridji ◽  
C. Houzard ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Ct Images ◽  
Volume Segmentation ◽  
Flt Pet ◽  
Pet Ct ◽  
Segmentation Methods

Reproducibility of volume and asymmetry measurements of hippocampus, amygdala, and entorhinal cortex on traveling volunteers: a multisite MP2RAGE prospective study

2020 ◽  
pp. 028418512096391
Author(s):  
Jiachen Du ◽  
Peipeng Liang ◽  
Hongjian He ◽  
Qiqi Tong ◽  
Ting Gong ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Healthy Aging ◽  
Structural Parameters ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Dice Similarity Coefficient ◽  
Automated Method ◽  
Segmentation Methods ◽  
Intraclass Correlations ◽  
The Impact

Background Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures—including the hippocampus, amygdala, and entorhinal cortex—in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases. Purpose To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset. Material and Methods Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data. Results ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349–0.529, DSC = 0.380–0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods. Conclusion This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies.

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