7 research outputs found
An Approach for Fast Segmentation of Lung Cancer Images
The lung tumor is a leading cause of death worldwide. This type of disease in which there is uncontrolled progression of abnormal cells in the lung, when untreated, can extend through the process of metastasis in nearby tissues and in different parts of the body [1]
Computational delineation and quantitative heterogeneity analysis of lung tumor on 18F-FDG PET for radiation dose-escalation
© 2018 The Author(s). Quantitative measurement and analysis of tumor metabolic activities could provide a more optimal solution to personalized accurate dose painting. We collected PET images of 58 lung cancer patients, in which the tumor exhibits heterogeneous FDG uptake. We design an automated delineation and quantitative heterogeneity measurement of the lung tumor for dose-escalation. For tumor delineation, our algorithm firstly separates the tumor from its adjacent high-uptake tissues using 3D projection masks; then the tumor boundary is delineated with our stopping criterion of joint gradient and intensity affinities. For dose-escalation, tumor sub-volumes with low, moderate and high metabolic activities are extracted and measured. Based on our quantitative heterogeneity measurement, a sub-volume oriented dose-escalation plan is implemented in intensity modulated radiation therapy (IMRT) planning system. With respect to manual tumor delineations by two radiation oncologists, the paired t-test demonstrated our model outperformed the other computational methods in comparison (p 0.05)
Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm.
International audienceA segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake
Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm.
International audienceA segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake
Regarding "Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm" By DP. Onoma et al.
International audienceThis letter to the editor adresses the issues of PET image segmentation and validation when implementing complex algorithms
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
Techniques for processing and analysing images and medical data have become
the main’s translational applications and researches in clinical and pre-clinical
environments. The advantages of these techniques are the improvement of diagnosis
accuracy and the assessment of treatment response by means of quantitative biomarkers
in an efficient way. In the era of the personalized medicine, an early and
efficacy prediction of therapy response in patients is still a critical issue.
In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high
quality detailed images and excellent soft-tissue contrast, while Computerized
Tomography (CT) images provides attenuation maps and very good hard-tissue
contrast. In this context, Positron Emission Tomography (PET) is a non-invasive
imaging technique which has the advantage, over morphological imaging techniques,
of providing functional information about the patient’s disease.
In the last few years, several criteria to assess therapy response in oncological
patients have been proposed, ranging from anatomical to functional assessments.
Changes in tumour size are not necessarily correlated with changes in tumour
viability and outcome. In addition, morphological changes resulting from therapy
occur slower than functional changes. Inclusion of PET images in radiotherapy
protocols is desirable because it is predictive of treatment response and provides
crucial information to accurately target the oncological lesion and to escalate the
radiation dose without increasing normal tissue injury. For this reason, PET may be
used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the
nature of PET images (low spatial resolution, high noise and weak boundary),
metabolic image processing is a critical task.
The aim of this Ph.D thesis is to develope smart methodologies applied to the
medical imaging field to analyse different kind of problematic related to medical
images and data analysis, working closely to radiologist physicians.
Various issues in clinical environment have been addressed and a certain amount
of improvements has been produced in various fields, such as organs and tissues
segmentation and classification to delineate tumors volume using meshing learning
techniques to support medical decision.
In particular, the following topics have been object of this study:
• Technique for Crohn’s Disease Classification using Kernel Support Vector
Machine Based;
• Automatic Multi-Seed Detection For MR Breast Image Segmentation;
• Tissue Classification in PET Oncological Studies;
• KSVM-Based System for the Definition, Validation and Identification of the
Incisinal Hernia Reccurence Risk Factors;
• A smart and operator independent system to delineate tumours in Positron
Emission Tomography scans;
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• Active Contour Algorithm with Discriminant Analysis for Delineating
Tumors in Positron Emission Tomography;
• K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor
Volumes;
• Tissue Classification to Support Local Active Delineation of Brain Tumors;
• A fully automatic system of Positron Emission Tomography Study
segmentation.
This work has been developed in collaboration with the medical staff and
colleagues at the:
• Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi
(DIBIMED), University of Palermo
• Cannizzaro Hospital of Catania
• Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale
delle Ricerche (CNR) of CefalĂą
• School of Electrical and Computer Engineering at Georgia Institute of
Technology
The proposed contributions have produced scientific publications in indexed
computer science and medical journals and conferences. They are very useful in
terms of PET and MRI image segmentation and may be used daily as a Medical
Decision Support Systems to enhance the current methodology performed by
healthcare operators in radiotherapy treatments.
The future developments of this research concern the integration of data acquired
by image analysis with the managing and processing of big data coming from a wide
kind of heterogeneous sources