Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

scholarly journals INTERESTING SPATIO-TEMPORAL REGION DISCOVERY COMPUTATIONS OVER GPU AND MAPREDUCE PLATFORMS

2015 ◽  
Vol II-4/W2 ◽  
pp. 35-41 ◽  
Author(s):  
M. McDermott ◽  
S. K. Prasad ◽  
S. Shekhar ◽  
X. Zhou
Keyword(s):  
Parallel Architecture ◽  
Computation Time ◽  
Data Sets ◽  
Temporal Region ◽  
Data Set ◽  
Atmospheric Sciences ◽  
3 Dimensional ◽  
Linear Speedup ◽  
Spatio Temporal ◽  
Mapreduce Model

Discovery of interesting paths and regions in spatio-temporal data sets is important to many fields such as the earth and atmospheric sciences, GIS, public safety and public health both as a goal and as a preliminary step in a larger series of computations. This discovery is usually an exhaustive procedure that quickly becomes extremely time consuming to perform using traditional paradigms and hardware and given the rapidly growing sizes of today’s data sets is quickly outpacing the speed at which computational capacity is growing. In our previous work (Prasad et al., 2013a) we achieved a 50 times speedup over sequential using a single GPU. We were able to achieve near linear speedup over this result on interesting path discovery by using Apache Hadoop to distribute the workload across multiple GPU nodes. Leveraging the parallel architecture of GPUs we were able to drastically reduce the computation time of a 3-dimensional spatio-temporal interest region search on a single tile of normalized difference vegetative index for Saudi Arabia. We were further able to see an almost linear speedup in compute performance by distributing this workload across several GPUs with a simple MapReduce model. This increases the speed of processing 10 fold over the comparable sequential while simultaneously increasing the amount of data being processed by 384 fold. This allowed us to process the entirety of the selected data set instead of a constrained window.

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

scholarly journals Multi-modal Visualization of Stroke Lesion CT-Imaging

2020 ◽  
Author(s):  
Kurt Schardt ◽  
Robin G. C. Maack ◽  
Dorothee Sauer ◽  
Hans Hagen ◽  
Gerik Scheuermann ◽  
...  
Keyword(s):  
Brain Lesion ◽  
Daily Routine ◽  
Data Sets ◽  
Computed Tomography Imaging ◽  
Data Set ◽  
Visualization System ◽  
Stroke Imaging ◽  
Suitable Treatment ◽  
Ct Data ◽  
The Brain

Stroke lesions are a result of a sudden cerebrovas-cular disease that cause a lack of blood supply to the brain. Clinicians aim to localize and quantify brain lesions by utilizing multi-modal CT (Computed Tomography) imaging in order to provide a suitable treatment. In clinical daily routine, neurologists review one modality at a time and a correlation between several modalities is hard to obtain. To better understand the effects of a stroke and identify the origin, we visualize the multi-modal CT data set of a patient by providing a multi-view visualization system. With this visualization we are able to provide a focus and overview of the multi-modal brain lesion imaging available of one patient that allows clinicians to correlate multi-modal stroke imaging. We show the applicability of the presented approach by applying it to real world patient data sets.

scholarly journals Three-Dimensional Stereoscopic Volume Rendering of Malignant Pleural Mesothelioma

2012 ◽  
Vol 97 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Nathan M Mollberg ◽  
Nigel M Parsad ◽  
Samuel G Armato ◽  
Janani Vigneswaran ◽  
Hedy L Kindler ◽  
...  
Keyword(s):  
Malignant Pleural Mesothelioma ◽  
Volume Rendering ◽  
Accurate Determination ◽  
Three Dimensional ◽  
Volumetric Analysis ◽  
Preoperative Imaging ◽  
Multidetector Row Computed Tomography ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Ct Data

Abstract Our objective was to investigate the application of three-dimensional (3D) stereoscopic volume rendering with perceptual colorization on preoperative imaging for malignant pleural mesothelioma. At present, we have prospectively enrolled 6 patients being considered for resection of malignant pleural mesothelioma that have undergone a multidetector-row computed tomography (CT) scan of the chest. The CT data sets were volume rendered without preprocessing. The resultant 3D rendering was displayed stereoscopically and used to provide information regarding tumor extent, morphology, and anatomic involvement. To demonstrate this technique, this information was compared with the corresponding two-dimensional CT grayscale axial images from two of these patients. Three-dimensional stereoscopic reconstructions of the CT data sets provided detailed information regarding the local extent of tumor that could be used for preoperative surgical planning. Three-dimensional stereoscopic volume rendering for malignant pleural mesothelioma is a novel approach. Combined with our innovative perceptual colorization algorithm, stereoscopic volumetric analysis potentially allows for the accurate determination of the extent of pleural mesothelioma with results difficult to duplicate using grayscale, multiplanar CT images.

Adherence to Initial Adjuvant Anastrozole Therapy Among Women With Early-Stage Breast Cancer

2008 ◽  
Vol 26 (4) ◽  
pp. 556-562 ◽  
Author(s):  
Ann H. Partridge ◽  
Andrea LaFountain ◽  
Erica Mayer ◽  
Brooke S. Taylor ◽  
Eric Winer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Early Stage Breast Cancer ◽  
Health Programs ◽  
Future Research ◽  
Data Sets ◽  
Data Set ◽  
Early Stage Disease ◽  
Patients At Risk ◽  
Program Plan

Purpose Previous research evaluating adherence to tamoxifen therapy among women with early-stage breast cancer has revealed adherence estimates ranging from 25% to 96%. No previous studies have focused on adherence to adjuvant aromatase inhibitors. Methods We used longitudinal claims data from three large commercial health programs to estimate adherence with anastrozole therapy among women with early-stage breast cancer. Adherence was defined as the proportion of days that patients had medication available over the observation period (ie, days covered); women with fewer than 80% of days covered were defined as nonadherent. Results More than 12,000 women in the databases were found to have new anastrozole prescription claims during the period of study: 1,498 women were classified as having early-stage disease in one commercial health program (Plan A) data set, 1,899 women in another program (Plan B) data set, and 8,994 women in MarketScan, a commercial data set made up of several health programs. Mean adherence over the first 12 months of therapy ranged from 82% to 88% in the three data sets. Between 19% and 28% of women had fewer than 80% of days covered. For women with 36 months of continuous eligibility, the mean adherence decreased each year, ranging from 78% to 86% in year 1 to 62% to 79% in year 3 within the three data sets. Conclusion A substantial proportion of women with early-stage breast cancer may be suboptimally adherent to adjuvant anastrozole therapy. Future research should focus on the identification of patients at risk for nonadherence with oral hormonal therapy for breast cancer and the development of interventions to improve adherence.

scholarly journals Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 730-733
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Census Data ◽  
Early Stage ◽  
Research Work ◽  
Numerical Data ◽  
Medical Data ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Data Set

The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.

scholarly journals Impact of respiratory motion on breast tangential radiotherapy using the field-in-field technique compared to irradiation using physical wedges

2014 ◽  
Vol 48 (1) ◽  
pp. 94-98 ◽  
Author(s):  
Hidekazu Tanaka ◽  
Shinya Hayashi ◽  
Kazuhiro Ohtakara ◽  
Hiroaki Hoshi
Keyword(s):  
Respiratory Motion ◽  
Early Stage ◽  
Free Breathing ◽  
Ct Images ◽  
Data Set ◽  
Acceptable Range ◽  
Treatment Plans ◽  
Prescription Dose ◽  
Ct Data ◽  
The Impact

Abstract Background. This study aimed to evaluate whether the field-in-field (FIF) technique was more vulnerable to the impact of respiratory motion than irradiation using physical wedges (PWs). Patients and methods. Ten patients with early stage breast cancer were enrolled. Computed tomography (CT) was performed during free breathing (FB). After the FB-CT data set acquisition, 2 additional CT scans were obtained during a held breath after light inhalation (IN) and light exhalation (EX). Based on the FB-CT images, 2 different treatment plans were created for the entire breast for each patient and copied to the IN-CT and EX-CT images. The amount of change in the volume of the target receiving 107%, 95%, and 90% of the prescription dose (V107%, V95%, and V90%, respectively), on the IN-plan and EX-plan compared with the FB-plan were evaluated. Results. The V107%, V95%, and V90% were significantly larger for the IN-plan than for the FB-plan in both the FIF technique and PW technique. While the amount of change in the V107% was significantly smaller in the FIF than in the PW plan, the amount of change in the V95% and V90% was significantly larger in the FIF plan. Thus, the increase in the V107% was smaller while the increases in the V95% and V90% were larger in the FIF than in the PW plan. Conclusions. During respiratory motion, the dose parameters stay within acceptable range irrespective of irradiation technique used although the amount of change in dose parameters was smaller with FIF technique.

scholarly journals Computer Aided Detection of Polyps in Whitelight- Colonoscopy Images using Deep Neural Networks

2019 ◽  
Vol 5 (1) ◽  
pp. 231-234 ◽  
Author(s):  
Thomas Wittenberg ◽  
Pascal Zobel ◽  
Magnus Rathke ◽  
Steffen Mühldorfer
Keyword(s):  
Neural Network ◽  
Image Data ◽  
Public Image ◽  
Computer Assisted ◽  
Data Sets ◽  
Polyp Detection ◽  
Data Set ◽  
Screening Programs ◽  
Computer Assisted Detection ◽  
Examination Process

AbstractEarly detection of polyps is one central goal of colonoscopic screening programs. To support gastroenterologists during this examination process, deep convolutional neural network can be applied for computer-assisted detection of neoplastic lesions. In this work, a Mask R-CNN architecture was applied. For training and testing, three independent colonoscopy data sets were used, including 2484 HD labelled images with polyps from our clinic, as well as two public image data sets from the MICCAI 2015 polyp detection challenge, consisting of 612 SD and 194 HD labelled images with polyps. After training the deep neural network, best results for the three test data sets were achieved in the range of recall = 0.92, precision = 0.86, F1 = 0.89 (data set A), rec = 0.86, prec = 0.80, F1 = 0.82 (data set B) and rec = 0.83, prec = 0.74, F1 = 0.79 (data set C).

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