15 research outputs found

    Brain Tumor Segmentation from Multi-Spectral Magnetic Resonance Image Data Using an Ensemble Learning Approach

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    The automatic segmentation of medical images represents a research domain of high interest. This paper proposes an automatic procedure for the detection and segmentation of gliomas from multi-spectral MRI data. The procedure is based on a machine learning approach: it uses ensembles of binary decision trees trained to distinguish pixels belonging to gliomas to those that represent normal tissues. The classification employs 100 computed features beside the four observed ones, including morphological, gradients and Gabor wavelet features. The output of the decision ensemble is fed to morphological and structural post-processing, which regularize the shape of the detected tumors and improve the segmentation quality. The proposed procedure was evaluated using the BraTS 2015 train data, both the high-grade (HG) and the low-grade (LG) glioma records. The highest overall Dice scores achieved were 86.5% for HG and 84.6% for LG glioma volumes

    Populációalapú „pilot” colorectalis rákszűrés eredményessége. Csongrád megye, 2015 | Efficacy of the population-based pilot colorectal screening program. Hungary, Csongrád county, 2015

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    Absztrakt: Bevezetés: Magyarországon a vastagbélrák rendkívül kedvezőtlen mortalitási adatainak javítása érdekében országos szintű colorectalis rákszűrő program bevezetését tervezik. Célkitűzés: Tanulmányunkban a 2015. év folyamán lezajlott, Csongrád megyei pilot szűrés során nyert tapasztalatokat összegeztük és értékeltük rövid távú hatékonyságát. Betegek és módszer: A kétlépcsős, széklet-okkultvér kimutatásán és kolonoszkópián alapuló szűrésben átlagos vastagbélrák-kockázatú, 50–70 év közötti, panaszmentes személyek vettek részt. A szűrés eredményességének értékelésénél figyelembe vettük a részvételi arányt, a vizsgálómódszerek pozitív prediktív értékét és a tumordetektációs rátát. A szűrés rövid távú hatékonyságát a Szegeden és vonzáskörzetében a 2013. és 2015. évi vastagbélrák incidenciájában és a kezdeti stádiumában bekövetkezett változás alapján határoztuk meg. Eredmények: A szűrőprogramba 22 130 személy kapott meghívást. A 46,4%-os részvételi arány mellett 1343 (13%) egyénnél bizonyult a székletminta „nem negatívnak”, közülük 766 beteg (7,5%) vállalta a szűrés keretein belül a kolonoszkópiát, illetve 711 betegnél került sor a vastagbél teljes átvizsgálására. A lejelentések alapján a vastagbéltükrözés során 358 (50,3%) személynél adenoma és 42 személynél (5,9%) rosszindulatú daganat igazolódott. A szűrés évében a rectumcarcinomák esetén nem, de a coloncarcinomák esetén szignifikáns eltérés mutatkozott az incidenciában (183 vs. 228; p = 0,026) és a daganatok mélységi kiterjedésében (p = 0,002). A nyirokcsomó-érintettség aránya 2015-ben szignifikánsan alacsonyabb volt (48,3% vs. 37,1%; p = 0,049). Következtetés: A Csongrád megyei populációs szintű colorectalis carcinoma szűrés a részvételi hajlandóság, valamint a daganatok incidenciájában és stádiumában bekövetkezett változás alapján, rövid távon egyértelműen sikeresnek bizonyult, ezért országos kiterjesztése szükséges. Orv Hetil. 2017; 158(42): 1658–1667. | Abstract: Introduction: In Hungary, a nationwide colorectal screening program is about to be introduced in order to improve the extremely high mortality rate of colorectal cancer (CRC). Aim: The aim of our study was to summarize experiences and assess short-term efficacy of the population-based pilot colorectal screening program in 2015 in Csongrád County, Hungary. Patients and method: Asymptomatic individuals between the ages of 50 and 70 with average risk of colorectal cancer participated in the program that was based on the two-step screening method (i.e. immune fecal blood test and colonoscopy). The short-term efficacy of the screening program was assessed as the change in total CRC incidence and initial tumor stage in the screening year (2015) compared to a control year (2013) in Szeged and its surroundings. Participation rate, positive predictive value of the screening methods and tumor detection rate was assessed. Results: 22,130 individuals were invited, the participation rate was 46.4%. Immune fecal blood test proved to be non-negative in 1,343 cases (13%), screening colonoscopy was performed in 766 of them (7.5%). Total colonoscopy was performed in 711 individuals. Based on the reports, adenoma was detected in 358 (50.3%) and malignancy in 42 (5.9%) individuals. In the background population, the incidence of colon cancer was significantly higher (183 vs. 228; p = 0.026) and was diagnosed at significantly earlier stage (p = 0.002). Lymph node involvement was significantly lower in 2015 (48.3% vs. 37.1%; p = 0.049). Conclusion: The Csongrád county population-based colorectal cancer screening was evidently successful on the short term considering participation rate, and the changes in CRC incidence and stage, thus its national extension is necessary. Orv Hetil. 2017; 158(42): 1658–1667

    Brain tumor segmentation from multispectral MR image data using ensemble learning methods

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    The number of medical imaging devices is quickly and steadily rising, generating an increasing amount of image records day by day. The number of qualified human experts able to handle this data cannot follow this trend, so there is a strong need to develop reliable automatic segmentation and decision support algorithms. The Brain Tumor Segmentation Challenge (BraTS), first organized seven years ago, provoked a strong intensification of the development of brain tumor detection and segmentation algorithms. Beside many others, several ensemble learning solutions have been proposed lately to the above mentioned problem. This study presents an evaluation framework developed to evaluate the accuracy and efficiency of these algorithms deployed in brain tumor segmentation, based on the BraTS 2016 train data set. All evaluated algorithms proved suitable to provide acceptable accuracy in segmentation, but random forest was found the best, both in terms of precision and efficiency

    A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement

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    The accurate and reliable segmentation of gliomas from magnetic resonance image (MRI) data has an important role in diagnosis, intervention planning, and monitoring the tumor’s evolution during and after therapy. Segmentation has serious anatomical obstacles like the great variety of the tumor’s location, size, shape, and appearance and the modified position of normal tissues. Other phenomena like intensity inhomogeneity and the lack of standard intensity scale in MRI data represent further difficulties. This paper proposes a fully automatic brain tumor segmentation procedure that attempts to handle all the above problems. Having its foundations on the MRI data provided by the MICCAI Brain Tumor Segmentation (BraTS) Challenges, the procedure consists of three main phases. The first pre-processing phase prepares the MRI data to be suitable for supervised classification, by attempting to fix missing data, suppressing the intensity inhomogeneity, normalizing the histogram of observed data channels, generating additional morphological, gradient-based, and Gabor-wavelet features, and optionally applying atlas-based data enhancement. The second phase accomplishes the main classification process using ensembles of binary decision trees and provides an initial, intermediary labeling for each pixel of test records. The last phase reevaluates these intermediary labels using a random forest classifier, then deploys a spatial region growing-based structural validation of suspected tumors, thus achieving a high-quality final segmentation result. The accuracy of the procedure is evaluated using the multi-spectral MRI records of the BraTS 2015 and BraTS 2019 training data sets. The procedure achieves high-quality segmentation results, characterized by average Dice similarity scores of up to 86%

    Brain Tumor Detection and Segmentation from Magnetic Resonance Image Data Using Ensemble Learning Methods

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    The steadily growing amount of medical image data requires automatic segmentation algorithms and decision support, because at a certain time, there will not be enough human experts to establish the diagnosis for every patient. It would be a good question to establish whether this day has already arrived or not. Computerized screening and diagnosis of brain tumor is an intensively investigated domain, especially since the first Brain Tumor Segmentation Challenge (BraTS) organized seven years ago. Several ensemble learning solutions have been proposed lately to the brain tumor segmentation problem. This paper presents an evaluation framework designed to test the accuracy and efficiency of ensemble learning algorithms deployed for brain tumor segmentation using the BraTS 2016 train data set. Within this category of machine learning algorithms, random forest was found the most appropriate, both in terms of precision and runtime

    A feature ranking and selection algorithm for brain tumor segmentation in multi-spectral magnetic resonance image data

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    Accuracy is the most important quality marker in medical image segmentation. However, when the task is to handle large volumes of data, the relevance of processing speed rises. In machine learning solutions the optimization of the feature set can significantly reduce the computational load. This paper presents a method for feature selection and applies it in the context of a brain tumor detection and segmentation problem in multi-spectral magnetic resonance image data. Starting from an initial set of 104 features involved in an existing ensemble learning solution that employs binary decision trees, a reduced set of features is obtained using a iterative algorithm based on a composite criterion. In each iteration, features are ranked according to the frequency of usage and the correctness of the decisions to which they contribute. Lowest ranked features are iteratively eliminated as long as the segmentation accuracy is not damaged. The final reduced set of 13 features provide the same accuracy in the whole tumor segmentation process as the initial one, but three times faster

    A study on histogram normalization for brain tumor segmentation from multispectral MR image data

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    Absolute values in magnetic resonance image data do not say anything about the investigated tissues. All these numerical values are relative, they depend on the imaging device and they may vary from session to session. Consequently, there is a need for histogram normalization before any other processing is performed on MRI data. The Brain Tumor Segmentation (BraTS) challenge organized yearly since 2012 contributed to the intensification of the focus on tumor segmentation techniques based on multi-spectral MRI data. A large subset of methods developed within the bounds of this challenge declared that they rely on a classical histogram normalization method proposed by Nyúl et al in 2000, which supposed that the corrected histogram of a certain organ composed of normal tissues only should be similar in all patients. However, this classical method did not count with possible lesions that can vary a lot in size, position, and shape. This paper proposes to perform a comparison of three sets of histogram normalization methods deployed in a brain tumor segmentation framework, and formulates recommendations regarding this preprocessing step
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