13 research outputs found
Unsupervised level set parameterization using multi-scale filtering
This paper presents a novel framework for unsupervised level set parameterization using multi-scale filtering. A standard multi-scale, directional filtering algorithm is used in order to capture the orientation coherence in edge regions. The latter is encoded in entropy-based image `heatmaps', which are able to weight forces guiding level set evolution. Experiments are conducted on two large benchmark databases as well as on real proteomics images. The experimental results demonstrate that the proposed framework is capable of accelerating contour convergence, whereas it obtains a segmentation quality comparable to the one obtained with empirically optimized parameterization
Self-adjusted active contours using multi-directional texture cues
Parameterization is an open issue in active contour research, associated with the cumbersome and time-consuming process of empirical adjustment. This work introduces a novel framework for self-adjustment of region-based active contours, based on multi-directional texture cues. The latter are mined by applying filtering transforms characterized by multi-resolution, anisotropy, localization and directionality. This process yields to entropy-based image “heatmaps”, used to weight the regularization and data fidelity terms, which guide contour evolution. Experimental evaluation is performed on a large benchmark dataset as well as on textured images. Τhe segmentation results demonstrate that the proposed framework is capable of accelerating contour convergence, maintaining a segmentation quality which is comparable to the one obtained by empirically adjusted active contours
State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor
Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images
Positron emission tomography (PET)-Computed tomography (CT) plays an important role in
cancer management. As a multi-modal imaging technique it provides both functional and anatomical
information of tumor spread. Such information improves cancer treatment in many ways. One
important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information
it provides helps radiation oncologists to better target the tumor region. However, currently
most tumor delineations in radiotherapy planning are performed by manual segmentation, which
consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to
locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues
like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In
order to address this issue, a novel co-segmentation method is proposed in this work to enhance
the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to
differentiate and segment tumor regions from normal regions. On a combined dataset containing
29 patients with lung tumor, the combined method shows good segmentation results as well as
good tumor recognition rate
image analysis and processing with applications in proteomics and medicine
This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours.
The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization.
The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics
Image Analysis and Processing With Applications in Proteomics and Medicine
Στην παρούσα διατριβή παρουσιάζονται αυτόματοι αλγόριθμοι ανάλυσης εικόνας για
την κατάτμηση διαφόρων τύπων εικόνων, με έμφαση στις εικόνες πρωτεομικής και
στις ιατρικές εικόνες. Οι προτεινόμενοι αλγόριθμοι βασίζονται στις αρχές των
παραμορφώσιμων μοντέλων. Η διατριβή εστιάζει σε δύο κυρίως στόχους: 1) στην
επίλυση του σημαντικού προβλήματος της αυτόματης παραμετροποίησης στην
κατάτμηση εικόνας, 2) στην διατύπωση ενός ολοκληρωμένου μοντέλου κατάτμησης
εικόνων πρωτεομικής. Η πρώτη συνεισφορά είναι ένα πρωτότυπο πλαίσιο αυτόματης
παραμετροποίησης των ενεργών περιγραμμάτων περιοχής. Το πλαίσιο εμπλουτίζει τα
αποτελέσματα με αντικειμενικότητα και απελευθερώνει τους τελικούς χρήστες από
την επίπονη διαδικασία της εμπειρικής ρύθμισης. Εφαρμόζεται σε διάφορους τύπους
ιατρικών εικόνων και παραμένει ανεπηρέαστο στις τροποποιήσεις των ρυθμίσεων των
συσκευών λήψης των εικόνων αυτών. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι
το προτεινόμενο πλαίσιο διατηρεί υψηλή την ποιότητα κατάτμησης, συγκρίσιμη με
εκείνη που επιτυγχάνεται με εμπειρική παραμετροποίηση. Η δεύτερη συνεισφορά
είναι ένα αυτόματο μοντέλο βασιζόμενο στα ενεργά περιγράμματα για την κατάτμηση
εικόνων πρωτεομικής. Το μοντέλο αντιμετωπίζει σημαντικά προβλήματα
συμπεριλαμβανομένων των γραμμών, τεχνουργημάτων, αχνών και επικαλυπτομένων
κηλίδων. Ακόμη, παρέχει εναλλακτική λύση στην επιρρεπή σε σφάλματα διαδικασία
της χειρωνακτικής επεξεργασίας που απαιτείται στα υπάρχοντα πακέτα λογισμικού.
Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο μοντέλο υπερτερεί
των υπαρχόντων πακέτων λογισμικού σε ποσοτικές μετρικές εντοπισμού και
κατάτμησης.This thesis introduces unsupervised image analysis algorithms for the
segmentation of several types of images, with an emphasis on proteomics and
medical images. Τhe presented algorithms are tailored upon the principles of
deformable models. Two objectives are pursued: 1) the core issue of
unsupervised parameterization in image segmentation, 2) the formulation of a
complete model for the segmentation of proteomics images. The first
contribution is a novel framework for automated parameterization of
region-based active contours. The presented framework endows segmentation
results with objectivity and sets domain users free from the cumbersome process
of empirical adjustment. It is applicable on various medical imaging modalities
and remains insensitive on alterations in the settings of acquisition devices.
The experimental results demonstrate that the presented framework maintains a
high segmentation quality, comparable to the one obtained with empirical
parameterization. The second contribution is an unsupervised active
contour-based model for the segmentation of proteomics images. The presented
model copes with crucial issues including streaks, artifacts, faint and
overlapping spots. Moreover, it provides an alternate to the error-prone
process of manual editing, required in state-of-the-art software packages. The
experimental results demonstrate that the proposed model outperforms software
packages in terms of detection and segmentation quantity metrics
Optimisation of Positron Emission Tomography based target volume delineation in head and neck radiotherapy
Automatic segmentation of tumours using Positron Emission Tomography
(PET) was recommended for radiotherapy treatment (RT) planning of head and neck
(H&N) cancer patients, and investigated in the scientific literature without reaching a
consensus on the optimal process. This project aimed at evaluating the performance of
PETCbased automatic segmentation (PETCAS) methods and developing an optimal PETC
AS process to be used at Velindre Cancer Centre (VCC). For this purpose, ten algorithms
were implemented to represent the most promising PETCAS approaches from a
systematic review of the literature. The algorithms’ performance was evaluated on
filled phantom inserts with variable size, geometry, tumour intensity and image noise.
The impact of thick insert plastic walls on both image quantification and segmentation
was thoroughly assessed. The PETCAS methods were further applied to realistic H&N
tumours, modelled using a printed subresolution sandwich phantom developed and
calibrated in house. Results showed that different PETCAS performed best for different
types of target objects. An Advanced decision TreeCbased Learning Algorithm for
Automatic Segmentation (ATLAAS) was therefore developed and validated for the
selection of the optimal PETCAS approach according to the target object characteristics.
Finally, a protocol was designed for the use of PETCAS within RT planning at VCC. The
protocol was used retrospectively on a group of 10 oropharyngeal cancer patients, and
the results highlighted the additional information brought by PET beyond anatomical
imaging. In a prospective study on 10 additional patients, PETCAS replaced manual
PET/CT delineation, and accounted for up to 33% of the modifications of manually
drawn CT/MRI contours to derive the final planning contour. This study demonstrated
the usefulness and reliability of the PETCAS method in RT planning, and led to
modifying the clinical workflow for H&N patients at VCC. This work has the potential to
be extended to other tumour sites and institutions
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