scholarly journals Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1098
Author(s):  
Gerardo Chacón ◽  
Johel E. Rodríguez ◽  
Valmore Bermúdez ◽  
Miguel Vera ◽  
Juan Diego Hernández ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computerized Tomography ◽  
Computational Models ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Ground Truth ◽  
Tumor Segmentation ◽  
Cancer Data ◽  
Stomach Tumor

Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.

scholarly journals Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1098 ◽  
Author(s):  
Gerardo Chacón ◽  
Johel E. Rodríguez ◽  
Valmore Bermúdez ◽  
Miguel Vera ◽  
Juan Diego Hernández ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computerized Tomography ◽  
Computational Models ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Ground Truth ◽  
Tumor Segmentation ◽  
Cancer Data ◽  
Stomach Tumor

Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.

scholarly journals Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

2017 ◽  
Vol 24 (5) ◽  
pp. 1065-1077 ◽  
Author(s):  
Talita Perciano ◽  
Daniela Ushizima ◽  
Harinarayan Krishnan ◽  
Dilworth Parkinson ◽  
Natalie Larson ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Ground Truth ◽  
Material Design ◽  
Material Structure ◽  
Segmentation Algorithms ◽  
Micro Tomography

Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM),k-means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

HER2 Expression in Gastric Cancer and Gastroesophageal Junction Cancer

2020 ◽  
Vol 22 (2) ◽  
pp. 79-82
Author(s):  
Md Azizur Rahman ◽  
Abdullah Md Abu Ayub Ansari ◽  
Kazi Mazharul Islam ◽  
Md Aminur Rahman ◽  
ABM Abdul Matin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Gastric Carcinoma ◽  
Social Impact ◽  
Gastroesophageal Junction ◽  
Treatment Protocol ◽  
Armed Forces ◽  
Her2 Expression ◽  
Her2 Positive ◽  
Cancer Data ◽  
Her2 Positivity

Background: Carcinoma of the stomach is a major cause of cancer mortality worldwide. Due to social impact of gastric carcinoma (GC), there is a need to stratify patients into appropriate screening, surveillance and treatment programs. Although histopathology remains the most reliable and less expensive method, numerous efforts have been made to identify and validate novel biomarkers to accomplish the goals. In recent years, several molecules have been identified and tested for their clinical relevace in GC management. Among the biomarkers with the exception of HER2, none of the biomarkers is currently used in clinical practice, and some of them were described in single studies. Materials and Methods: This prospective type of observational study was performed in the Department of Surgery, Dhaka Medical College Hospital, Dhaka, 6 months from approval of protocol. Total 45 consecutive patients aged 18 years and above without consideration of gender were selected purposefully. Every patient was evaluated by clinical examination, appropriate investigations and after a confirm diagnosis of the tissue from the cancer. All patients have undergone operative intervention and Gastrectomy specimens were subtotal (including cardiac and pylorus), subtotal (including the pylorus), total radical gastrectomy and oesophago-gastrectomy sample. All specimens obtained were immersed in 10% formalin. Samples of whom were sent to the department of pathology, DMCH for histopathology examination. Portion of representative tissue/block was sent to AFIP (Armed Forces Institute of Pathology, Dhaka) for immunohistochemistry to find out the HER2 expression in gastric cancer and gastro-oesophageal cancer. Data was collected in a pre-designed questionnaire by face to face interview. Result and observation: In this study when 45 cases were categorized according to WHO grading system it was observed that majority (30) patients were found in grade II, among them 3(10%) were HER2 positive. But with grade III tumour the HER2 positivity were found more i,e; 37.5% (3/8). Grade- I tumor show HER2 neu expression 28.57% (2/7) and according to location most of the cases with HER2 positive expression was located in the gastro-esophageal junction which is 27.27% (3/11) than gastric carcinoma which is 14.70% (5/34). Conclusion: Most of the patients of gastric and gastrooesophageal junction adenocarcinoma are diagnosed at a very late stage, so they require special attention in treatment protocol, including chemotherapy and immunotherapy for increasing their survivability. The study showed with poorly differentiated (high grade) tumour, the HER2 positivity were found more. Journal of Surgical Sciences (2018) Vol. 22 (2) : 79-82

scholarly journals IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii359-iii359
Author(s):  
Lydia Tam ◽  
Edward Lee ◽  
Michelle Han ◽  
Jason Wright ◽  
Leo Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Posterior Fossa ◽  
Ground Truth ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Study Cohort ◽  
Navigation Systems ◽  
Tumor Types ◽  
Deep Learning Model

Abstract BACKGROUND Brain tumors are the most common solid malignancies in childhood, many of which develop in the posterior fossa (PF). Manual tumor measurements are frequently required to optimize registration into surgical navigation systems or for surveillance of nonresectable tumors after therapy. With recent advances in artificial intelligence (AI), automated MRI-based tumor segmentation is now feasible without requiring manual measurements. Our goal was to create a deep learning model for automated PF tumor segmentation that can register into navigation systems and provide volume output. METHODS 720 pre-surgical MRI scans from five pediatric centers were divided into training, validation, and testing datasets. The study cohort comprised of four PF tumor types: medulloblastoma, diffuse midline glioma, ependymoma, and brainstem or cerebellar pilocytic astrocytoma. Manual segmentation of the tumors by an attending neuroradiologist served as “ground truth” labels for model training and evaluation. We used 2D Unet, an encoder-decoder convolutional neural network architecture, with a pre-trained ResNet50 encoder. We assessed ventricle segmentation accuracy on a held-out test set using Dice similarity coefficient (0–1) and compared ventricular volume calculation between manual and model-derived segmentations using linear regression. RESULTS Compared to the ground truth expert human segmentation, overall Dice score for model performance accuracy was 0.83 for automatic delineation of the 4 tumor types. CONCLUSIONS In this multi-institutional study, we present a deep learning algorithm that automatically delineates PF tumors and outputs volumetric information. Our results demonstrate applied AI that is clinically applicable, potentially augmenting radiologists, neuro-oncologists, and neurosurgeons for tumor evaluation, surveillance, and surgical planning.

scholarly journals Rapid imaging of special nuclear materials for nuclear nonproliferation and terrorism prevention

2021 ◽  
Vol 7 (21) ◽  
pp. eabg3032
Author(s):  
Jana Petrović ◽  
Alf Göök ◽  
Bo Cederwall
Keyword(s):  
Nuclear Physics ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Nuclear Fission ◽  
Machine Learning Techniques ◽  
Nuclear Materials ◽  
Prototype System ◽  
Fast Time ◽  
Gamma Emission ◽  
Special Nuclear Materials

We introduce a neutron-gamma emission tomography (NGET) technique for rapid detection, three-dimensional imaging, and characterization of special nuclear materials like weapons-grade plutonium and uranium. The technique is adapted from fundamental nuclear physics research and represents a previously unexplored approach to the detection and imaging of small quantities of these materials. The method is demonstrated on a radiation portal monitor prototype system based on fast organic scintillators, measuring the characteristic fast time and energy correlations between particles emitted in nuclear fission processes. The use of these correlations in real time in conjunction with modern machine learning techniques provides unprecedented imaging efficiency and high spatial resolution. This imaging modality addresses global security threats from terrorism and the proliferation of nuclear weapons. It also provides enhanced capabilities for addressing different nuclear accident scenarios and for environmental radiological surveying.

scholarly journals Magnetic particle imaging of particle dynamics in complex matrix systems

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sebastian Draack ◽  
Meinhard Schilling ◽  
Thilo Viereck
Keyword(s):  
Magnetic Particle ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Local Environment ◽  
Relaxation Mechanism ◽  
Harmonic Spectrum ◽  
Magnetic Particle Imaging ◽  
Matrix Interactions ◽  
Tracer Distribution ◽  
Particle Imaging

Abstract Magnetic particle imaging (MPI) is a young imaging modality for biomedical applications. It uses magnetic nanoparticles as a tracer material to produce three-dimensional images of the spatial tracer distribution in the field-of-view. Since the tracer magnetization dynamics are tied to the hydrodynamic mobility via the Brownian relaxation mechanism, MPI is also capable of mapping the local environment during the imaging process. Since the influence of viscosity or temperature on the harmonic spectrum is very complicated, we used magnetic particle spectroscopy (MPS) as an integral measurement technique to investigate the relationships. We studied MPS spectra as function of both viscosity and temperature on model particle systems. With multispectral MPS, we also developed an empirical tool for treating more complex scenarios via a calibration approach. We demonstrate that MPS/MPI are powerful methods for studying particle-matrix interactions in complex media.

Acceleration of the EM-like reconstruction method for diffuse optical tomography with ordered-subsets method

2014 ◽  
Vol 22 (3) ◽  
Author(s):  
Caifang Wang
Keyword(s):  
Numerical Experiments ◽  
Imaging Modality ◽  
Random Medium ◽  
Optical Tomography ◽  
Back Propagation ◽  
Three Dimensional ◽  
Diffuse Optical Tomography ◽  
Optical Parameters ◽  
Reconstruction Method ◽  
Boundary Measurements

Abstract.Diffuse optical tomography (DOT) is an optical imaging modality, which provides the spatial distribution of the optical parameters inside a random medium. A propagation back-propagation method named EM-like reconstruction method for stationary DOT problem has been proposed yet. This method is really time consuming. Hence the ordered-subsets (OS) technique for this reconstruction method is studied in this paper. The boundary measurements of DOT are grouped into nonoverlapping and overlapping ordered sequence of subsets with random partition, sequential partition and periodic partition, respectively. The performance of OS methods is compared with the standard EM-like reconstruction method with two-dimensional and three-dimensional numerical experiments. The numerical experiments indicate that reconstruction of nonoverlapping subsets with periodic partition, overlapping subsets with periodic partition and standard EM-like method provide very similar acceptable reconstruction results. However, reconstruction of nonoverlapping subsets with periodic partition spends a minimum of time to get proper results.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

Sign in / Sign up

Export Citation Format

Share Document