scholarly journals Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Daniel Markel ◽  
Curtis Caldwell ◽  
Hamideh Alasti ◽  
Hany Soliman ◽  
Yee Ung ◽  
...  
Keyword(s):  
Lung Carcinoma ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Ground Truth ◽  
Standard Uptake Value ◽  
Structural Features ◽  
3D Texture ◽  
Positron Emission ◽  
Fuzzy Clustering Method ◽  
And Performance

Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.

scholarly journals LIVER TUMOR SEGMENTATION CT DATA BASED ON ALEXNET-LIKE CONVOLUTION NEURAL NETS

2016 ◽  
Author(s):  
Alexandr N. Korabelnikov ◽  
◽  
Alexandr V. Kolsanov ◽  
Sergey S. Chaplygin ◽  
Pavel M. Zelter ◽  
...  
Keyword(s):  
Liver Tumors ◽  
Automatic Segmentation ◽  
A Priori ◽  
Ground Truth ◽  
Neural Nets ◽  
Average Error ◽  
Tumor Segmentation ◽  
Ct Data ◽  
And Performance ◽  
Anatomical Information

Anatomical structure segmentation on computed tomography (CT) is the key stage in medical visualization and computer diagnosis. Tumors are one of types of internal structures, for which the problem of automatic segmentation today has no solution fully satisfying by quality. The reason is high variance of tumor’s density and inability of using a priori anatomical information about shape. In this paper we propose automatic method of liver tumors segmentation based on convolution neural nets (CNN). Studying and validation have been performed on set of CT with liver and tumors segmentation ground truth. Average error (VOE) by cross-validation is 17.3%. Also there were considered algorithms of pre- and post-processing which increase accuracy and performance of segmentation procedure. Particularly the acceleration of the segmentation procedure with negligible decrease of quality has been reached 6 times.

scholarly journals Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting

2021 ◽  
Author(s):  
Andrei Iantsen ◽  
Marta Ferreira ◽  
Francois Lucia ◽  
Vincent Jaouen ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Truth ◽  
Dice Similarity Coefficient ◽  
Tumor Uptake ◽  
Shape Priors ◽  
Positron Emission ◽  
Locally Adaptive ◽  
Fully Automatic ◽  
Development And Validation ◽  
Anatomical Information

Abstract Purpose In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics. Methods In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing). Results The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training. Conclusion The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.

scholarly journals Characterization and Pilot Human Trial of Dedicated Breast Ring Positron Emission Tomography (BRPET) System

Instruments ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 30
Author(s):  
Andrew M. Polemi ◽  
Annie K. Kogler ◽  
Patrice K. Rehm ◽  
Luke Lancaster ◽  
Heather R. Peppard ◽  
...  
Keyword(s):  
Human Study ◽  
Ground Truth ◽  
Imaging Geometry ◽  
System Sensitivity ◽  
Testing Procedures ◽  
Dedicated Breast Pet ◽  
Positron Emission ◽  
And Performance ◽  
Sensitivity Specificity ◽  
Light Guides

We describe the design and performance of BRPET, a novel dedicated breast PET (dbPET) scanner designed to maximize visualization of posterior regions of the breast. BRPET uses prone imaging geometry and a 12-module detector ring built from pixelated LYSO crystals coupled to position sensitive photomultiplier tubes (PSPMTs). Optical coupling via slanted plastic fiber optic light guides permits partial insertion of the crystals into the exam table’s breast aperture. Image quality testing procedures were adapted from the NEMA NU4-2008 protocol. Two additional phantom tests quantified the posterior extent of the usable volume of view (VoV). BRPET axial, radial, and tangential FWHM spatial resolutions at the isocenter were 1.8, 1.7, and 1.9 mm, respectively. The peak absolute system sensitivity was 0.97% using an energy window of 460–562 keV. The peak noise equivalent counting rate was 5.33 kcps at 21.6 MBq. The scanner VoV extends to within ~6 mm of the plane defining the location of the chest wall. A pilot human study (n = 10) compared the diagnostic performance of FDG-BRPET to that of contrast enhanced MRI (CEMRI), with biopsy as ground truth. Averaged over three expert human observers, the sensitivity/specificity for BRPET was 0.93/1.0, compared to 1.0/0.25 for CEMRI.

18F-Deoxyglucose PET for the staging of oesophageal cancer

2003 ◽  
Vol 42 (03) ◽  
pp. 90-93 ◽  
Author(s):  
N. Döbert ◽  
O. Rieker ◽  
W. Kneist ◽  
St. Mose ◽  
A. Teising ◽  
...  
Keyword(s):  
Oesophageal Cancer ◽  
Squamous Cell ◽  
Distal Part ◽  
Standard Uptake Value ◽  
Squamous Cell Carcinomas ◽  
Tumour Grading ◽  
Positron Emission ◽  
Group A ◽  
The Mean ◽  
Drop Outs

SummaryAim: Evaluation of the influence of histopathologic sub-types and grading of primaries of oesophageal cancer, relative to their size and location, on the uptake of 18F-deoxyglucose (FDG) as measured by positron emission tomography (PET). Methods: 50 consecutive patients were evaluated. There were four drop-outs due to previous surgical and/or chemotherapeutical treatments and thus in 46 patients (28 squamous cell carcinomas and 18 adenocarcinomas) a pretherapeutic PET evalution of the primary including a standard uptake value (SUV) was obtained. In 42 cases data on tumour grading were available also. Results: Squamous cell carcinomas (SCC) were in 7/13/8 cases located in the proximal, medial and distal part of the oesophagus, respectively the grading was Gx in 3, G 2 in 12, G2-3 in 7, and G3 in 6 cases. The SUVmax showed a mean of 6.5 ± 2.8 (range 1.7-13.5). Adenocarcinomas (ACA) were located in the medial oesophagus in two cases and otherwise in its distal parts. Grading was Gx in one, G2 in 4, G2-3 in 3, G3 in 3, G3-4 in 3, and G4 in one case. The mean SUVmax was 5.2 ± 3.2 (range 1-13.6) and this was not significantly different from the SCC. Concerning the tumour grading there was a slight, statistically not relevant trend towards higher SUVmax in more dedifferentiated cancer. Discussion: SCC and ACA of the oesophagus show no relevant differences in the FDG-uptake. While there was a significant variability of tumour uptake in the overall study group, a correlation of SUV and tumour grading was not found.

Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

2020 ◽  
Vol 77 (4) ◽  
pp. 1609-1622
Author(s):  
Franziska Mathies ◽  
Catharina Lange ◽  
Anja Mäurer ◽  
Ivayla Apostolova ◽  
Susanne Klutmann ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Fdg Pet ◽  
False Negative ◽  
Ground Truth ◽  
Etiological Diagnosis ◽  
Geriatric Unit ◽  
Positron Emission ◽  
The Brain ◽  
Hospitalized Geriatric Patients

Background: Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. Objective: This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. Methods: The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. Results: FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). Conclusion: Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

Carpal Bone Segmentation Using Fully Convolutional Neural Network

2019 ◽  
Vol 15 (10) ◽  
pp. 983-989
Author(s):  
Liang Kim Meng ◽  
Azira Khalil ◽  
Muhamad Hanif Ahmad Nizar ◽  
Maryam Kamarun Nisham ◽  
Belinda Pingguan-Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Bone Age ◽  
Image Feature ◽  
Qualitative Assessment ◽  
Carpal Bone ◽  
Radiographic Images ◽  
Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

scholarly journals Texture-Based Analysis of 18F-Labeled Amyloid PET Brain Images

2021 ◽  
Vol 11 (5) ◽  
pp. 1991
Author(s):  
Alexander P. Seiffert ◽  
Adolfo Gómez-Grande ◽  
Eva Milara ◽  
Sara Llamas-Velasco ◽  
Alberto Villarejo-Galende ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Standardized Uptake Value ◽  
Texture Features ◽  
Roc Curves ◽  
Significant Feature ◽  
Study Cohort ◽  
Amyloid Pet ◽  
Reference Region ◽  
Brain Images ◽  
Positron Emission

Amyloid positron emission tomography (PET) brain imaging with radiotracers like [18F]florbetapir (FBP) or [18F]flutemetamol (FMM) is frequently used for the diagnosis of Alzheimer’s disease. Quantitative analysis is usually performed with standardized uptake value ratios (SUVR), which are calculated by normalizing to a reference region. However, the reference region could present high variability in longitudinal studies. Texture features based on the grey-level co-occurrence matrix, also called Haralick features (HF), are evaluated in this study to discriminate between amyloid-positive and negative cases. A retrospective study cohort of 66 patients with amyloid PET images (30 [18F]FBP and 36 [18F]FMM) was selected and SUVRs and 6 HFs were extracted from 13 cortical volumes of interest. Mann–Whitney U-tests were performed to analyze differences of the features between amyloid positive and negative cases. Receiver operating characteristic (ROC) curves were computed and their area under the curve (AUC) was calculated to study the discriminatory capability of the features. SUVR proved to be the most significant feature among all tests with AUCs between 0.692 and 0.989. All HFs except correlation also showed good performance. AUCs of up to 0.949 were obtained with the HFs. These results suggest the potential use of texture features for the classification of amyloid PET images.

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