234 research outputs found
PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques
Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addresse
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
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
Recommended from our members
Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring
INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume
delineation remains one of the greatest sources of error in the radiotherapy delivery process,
which can lead to poor tumour control probability and impact clinical outcome. Contouring
assessments are performed to ensure high quality of target volume definition in clinical trials
but this can be subjective and labour-intensive.
This project addresses the hypothesis that computational segmentation techniques, with a given
prior, can be used to develop an image-based tumour delineation process for contour
assessments. This thesis focuses on the exploration of the segmentation techniques to develop
an automated method for generating reference delineations in the setting of advanced lung
cancer. The novelty of this project is in the use of the initial clinician outline as a prior for
image segmentation.
METHODS: Automated segmentation processes were developed for stage II and III non-small
cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed
segmentation, two active contour approaches (edge- and region-based) and graph-cut applied
on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from
normal tissues based on texture features was also investigated.
RESULTS: 63 cases were used for development and training. Segmentation and classification
performance were evaluated on an independent test set of 16 cases. Edge-based active contour
segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut
at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07,
with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec
per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation
leakages at the mediastinum were observed.
In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and
15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher
misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the
analysis of the tumour boundary.
CONCLUSIONS: Conventional image-based segmentation techniques with the application of
priors are useful in automatic segmentation of tumours, although further developments are
required to improve their performance. Texture classification can be useful in distinguishing
tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more
difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou
Artificial Intelligence in Radiation Therapy
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy
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