202 research outputs found
Textural features for bladder cancer definition on CT images
Genitourinary cancer refers to the presence of tumours in the genital or urinary organs such as
bladder, kidney and prostate. In 2008 the worldwide incidence of bladder cancer was 382,600
with a mortality of 150,282. Radiotherapy is one of the main treatment choices for genitourinary
cancer where accurate delineation of the gross tumour volume (GTV) on computed tomography
(CT) images is crucial for the success of this treatment. Limited CT resolution and
contrast in soft tissue organs make this difficult and has led to significant inter- and intra- clinical
variability in defining the extent of the GTV, especially at the junctions of different organs. In
addition the introduction of new imaging techniques and modalities has significantly increased
the number of the medical images that require contouring. More advanced image processing
is required to help reduce contouring variability and assist in handling the increased volume of
data.
In this thesis image analysis methodologies were used to extract low-level features such as
entropy, moment and correlation from radiotherapy planning CT images. These distinctive
features were identified and used for defining the GTV and to implement a fully-automatic
contouring system. The first key contribution is to demonstrate that second-order statistics
from co-occurrence matrices (GTSDM) give higher accuracy in classifying soft tissue regions
of interest (ROIs) into GTV and non-GTV. Loadings of the principal components (PCs) of
the GTSDM features were found to be consistent over different patients. Exhaustive feature
selection suggested that entropies and correlations produced consistently larger areas under
receiver operating characteristic (AUROC) curves than first-order features.
The second significant contribution is to demonstrate that in the bladder-prostate junction,
where the largest inter-clinical variability is observed, the second-order principal entropy from
stationery wavelet denoised CT images (DPE) increased the saliency of the bladder prostate
junction. As a result thresholding of the DPE produced good agreement between gold standard
clinical contours and those produced by this approach with Dice coefficients.
The third contribution is to implement a fully automatic and reproducible system for bladder
cancer GTV auto-contouring based on classifying second-order statistics. The Dice similarity
coefficients (DSCs) were employed to evaluate the automatic contours. It was found that in the
mid-range of the bladder the automatic contours are accurate, but in the inferior and superior
ends of bladder automatic contours were more likely to have small DSCs with clinical contours,
which reconcile with the fact of clinical variability in defining GTVs. A novel male bladder
probability atlas was constructed based on the clinical contours and volume estimation from
the classification results. Registration of the classification results with this probabilistic atlas
consistently increases the DSCs of the inferior slices
Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature
Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naive patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images
Quantitative imaging in radiation oncology
Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care
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;
3
• 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 Cefalù
• 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
Radiomics and prostate MRI: Current role and future applications
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer
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