A Teaching Resource for Complex Systems, Machine Learning and Computational Biology

This work presents a collection of teaching materials related to complex systems, machine learning, computational biology and computational immunology

Beauty of Life in Dynamical Systems: Philosophical Musings and Resources for Students

Information plays a key role in life and in complex biological systems, and dynamical systems underlie and can be used to represent many complex systems. Indeed, dynamical systems and information processing capabilities may be the hallmarks of life-like systems. In this paper we combine dynamical systems with a computational framework to generate art. The framework can be used to generate aesthetically appealing forms of life-like systems. Our work suggests that we may need an ``aesthetic sense\u27\u27 to recognize life that we have not seen before. We also provide teaching resources for students in schools and undergraduate institutions

A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Robust image segmentation applied to magnetic resonance and ultrasound images of the prostate

Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonance images (MRI) facilitates volume estimation, multi-modal image registration, surgical planing and image guided prostate biopsies. The objective of this thesis is to develop computationally efficient prostate segmentation algorithms in both TRUS and MRI image modalities. In this thesis we propose a probabilistic learning approach to achieve a soft classification of the prostate for automatic initialization and evolution of a deformable model for prostate segmentation. Two deformable models are developed for the TRUS segmentation. An explicit shape and region prior based deformable model and an implicit deformable model guided by an energy minimization framework. Besides, in MRI, the posterior probabilities are fused with the soft segmentation coming from an atlas segmentation and a graph cut based energy minimization achieves the final segmentation. In both image modalities, statistically significant improvement are achieved compared to current works in the literature.La segmentació de la pròstata en imatge d'ultrasò (US) i de ressonància magnètica (MRI) permet l'estimació del volum, el registre multi-modal i la planificació quirúrgica de biòpsies guiades per imatge. L'objectiu d'aquesta tesi és el desenvolupament d'algorismes automàtics per a la segmentació de la pròstata en aquestes modalitats. Es proposa un aprenentatge automàtic inical per obtenir una primera classificació de la pròstata que permet, a continuació, la inicialització i evolució de diferents models deformables. Per imatges d'US, es proposen un model explícit basat en forma i informació regional i un model implícit basat en la minimització d'una funció d'energia. En MRI, les probalitats inicials es fusionen amb una imatge de probabilitat provinent d'una segmentació basada en atlas, i la minimització es realitza mitjançant tècniques de grafs. El resultat final és una significant millora dels algorismes actuals en ambdues modalitats d'imatge

Segmentation d'images robuste appliqué à l'imagerie par résonance magnétique et l'échographie de la prostate

Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonanceimages (MRI) facilitates volume estimation, multi-modal image registration, surgicalplaning and image guided prostate biopsies. The objective of this thesis is to developshape and region prior deformable models for accurate, robust and computationallyefficient prostate segmentation in TRUS and MRI images. Primary contributionof this thesis is in adopting a probabilistic learning approach to achieve soft classificationof the prostate for automatic initialization and evolution of a shape andregion prior deformable models for prostate segmentation in TRUS images. Twodeformable models are developed for the purpose. An explicit shape and regionprior deformable model is derived from principal component analysis (PCA) of thecontour landmarks obtained from the training images and PCA of the probabilitydistribution inside the prostate region. Moreover, an implicit deformable model isderived from PCA of the signed distance representation of the labeled training dataand curve evolution is guided by energy minimization framework of Mumford-Shah(MS) functional. Region based energy is determined from region based statistics ofthe posterior probabilities. Graph cut energy minimization framework is adoptedfor prostate segmentation in MRI. Posterior probabilities obtained in a supervisedlearning schema and from a probabilistic segmentation of the prostate using an atlasare fused in logarithmic domain to reduce segmentation error. Finally a graphcut energy minimization in the stochastic framework achieves prostate segmentationin MRI. Statistically significant improvement in segmentation accuracies areachieved compared to some of the works in literature. Stochastic representation ofthe prostate region and use of the probabilities in optimization significantly improvesegmentation accuraciesLa segmentació de la pròstata en imatges d’ecografia transrectal (TRUS) i en imatgesde ressonáncia magnètica (RM) facilita l’estimació del volum d’aquesta glàndula,el registre d’imatges entre ambdues modalitats, així com la planificació quirrgica debiòpsies guiades per imatge. L’objectiu d’aquesta tesi, doncs, és el desenvolupamentd’eines automàtiques per a una segmentació de la pròstata de manera precisa,robusta i computacionalment eficient en ambdues modalitats d’imatges.La contribució principal d’aquest tesi és la segmentació de les imatges ecogràfiquesde la pròstata. El mètode proposat es basa en dos passos ben diferenciats. Primer, através d’un aprenentatge probabilístic inicial, s’aconsegueix una primera localitzacióaproximada de la pròstata i que serveix per, en un segon pas, inicialitzar i permetreevolucionar de manera automàtica dos models deformables independents, guiats apartir de la informació de forma i regió de la pròstata estimada en el primer pas. Elprimer model deformable s’obté explícitament a partir de l’anàlisi de componentsprincipals (PCA) d’un conjunt de punts del contorn, que permet modelar la formade la pròstata, i de l’anàlisi PCA de la distribució de probabilitat dins de la regióprostàtica, que permet modelar la textura d’aquesta. Un tercer anàlisi PCA permetcorrelacionar ambdues distribucions. D’altra banda, un segon model deformable esderiva implícitament de l’anàlisi PCA de la funció distància obtinguda amb el conjuntde dades d’entrenament etiquetades. La consegüent evolució d’aquesta corbas’obté mitjanant la minimització del funcional Mumford-Shah, el qual es basa en unconjunt d’estadístics regionals obtinguts a partir de l’estimació de les probabilitatsa posteriori de les regions internes i externes de la pròstata.La segona contribució d’aquesta tesi és la segmentació automàtica de la pròstataen imatges 3D de RM. De manera similar a les imatges ecogràfiques, el sistemacombina les probabilitats d’un aprenentatge supervisat amb una segmentació inicial,en aquest cas, obtinguda a partir d’un atles probabilístic creat amb els volumsd’entrenament. La segmentació final s’obté a través d’una minimització basada engrafs.El resultat final és, doncs, el desenvolupament d’eines que permeten una segmentació acurada i robusta de la pròstata tant en imatges ecogròfiques com deressonòncia magnètica, millorant de forma substancial i significant la precisió delsmètodes desenvolupats fins a l’actualitat[...] L’utilisation d’images ETR pour la biopsie est maintenant une norme suivie par les urologues pour le dépistage du cancer de la prostate. Toutefois, l’imagerie par résonance magnétique (IRM) offre un meilleur contraste des tissus mous par rapport aux images ETR. Ainsi, certaines tumeurs malignes visibles par l’IRM ne le sont pas avec les images ETR comme illustré par l’image de la figure 1. En fusionnant les deux modalités IRM et échographie transrectale, il est possible de développer des outils performants de diagnostic. C’est dans ce contexte que s’inscrit le projet PROSCAN qui est une collaboration entre le centre de recherche VICOROB (Computer Vision and Robotics Group) de l’université de Gérone et le Girona Magnetic Resonance Center du CHU de Gérone. [...] .. L’objectif principal de cette thèse est de développer des méthodes de segmentation précises et rapides de la prostate dans les images IRM ET ETR afin de faciliter la fusion d’images multimodales dans le cadre du projet PROSCAN. [...] Nous avons commencé notre travail par une étude approfondie des méthodes de segmentation dans les deux modalités échographie transrectale et IRM. Les principales similitudes et les différences entre les diverses méthodes, leurs forces et faiblesse sont été analysées. Les méthodes de segmentation de la prostate peuvent être regroupées dans quatre catégories différentes, selon les informations utilisées pour guider la segmentation [...] L’analyse des méthodes de segmentation montre que les approches qui combinent les informations de forme et de contour donnent les meilleurs résultats. Aussi, nous proposons d’utiliser le modèle AAM (Actice Appearance Model) qui a prouvé son efficacité pour la segmentation de la prostate dans les image d’échographietransrectale. Le modèle AAM permet de combiner les informations de forme et d’apparence en une unique fonction de coût à optimiser. De plus, l’étape d’optimisation par descente de gradient faite hors-ligne réduit considérablement les temps de calcul.Les images obtenues par échographie transrectale possèdent généralement une faible qualité ainsi qu’un faible contraste. Pour améliorer la robustesse de notre méthode de segmentation, nous introduisons des caractéristiques de texture extraits avec les ondelettes de Haar et des filtres en quadrature. Les résultats obtenus montrent que cette information de texture accroit la précision de la segmentation. Parailleurs, l’augmentation du temps de calcul due à l’utilisation des filtres est compensé par l’augmentation de la précision.Pour une initialisation automatique, nous avons développé un modèle probabiliste basé sur une classification supervisée. Un classifieur est construit à partird’un ensemble d’images d’apprentissage manuellement segmentées. Ce classifieur est utilisé pour obtenir une pré-segmentation de la prostate dans l’image ETR dans laquelle on attribue à chaque pixel une probabilité d’appartenance à la prostate. Unnouveau modèle AAM est ensuite construit dans lequel les intensités sont remplacéespar les probabilités obtenues à l’etape précédente. Les résultats obtenus montrent que cette approche permet une initialisation automatique tout en améliorant laprécision de la segmentation.Enfin, pour obtenir un modèle plus robuste nous avons utilisé la fonctionnelle de Mumford-Shah qui permet de définir une fonction de coût à optimiser comprenant à la fois les informations d’apparence, de forme et de topologie locale de laprostate. Les nombreux résultats qualitatifs et quantitatifs présentés dans la suite de ce manuscrit montrent que notre méthode donne de meilleurs résultats comparé à diverses autres approches

Beauty of Life in Dynamical Systems: an Aesthetic Viewpoint of Life

Information plays a key role in life and complex biological systems. It is hypothesized that information processing capabilities distinguish life from other so-called non-living matter. Dynamical systems underlie and can be used to represent many complex life-like systems. Dynamical systems and information processing may be the hallmarks of life-like systems. We combine dynamical systems with a computational framework to generate art. The framework can be used to generate aesthetically appealing forms of life-like systems. Our work suggests that we may need an “aesthetic sense” to recognize life we have never seen before. This aesthetic view also allows us to appreciate the beauty of life-like systems, life-forms around us, and their intimate connections with dynamical systems. This perspective can give us a sense that every part of the Universe computes and that the entire Universe is alive and has intelligence. We hope this will give humanity a new sense of purpose, help us appreciate our place in the Universe and also give a renewed thrust to conservation efforts to save our planet

A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance

International audienceProstate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentation. The proposed method achieves a mean Dice similarity coefficient value of 0.96±0.01, and a mean absolute distance value of 0.80±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. Our proposed model is automatic, and performs accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts

Prostate Segmentation with Local Binary Patterns Guided Active Appearance Models

International audienceReal-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the local- ization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient segmentation of the prostate in TRUS images could be challenging in the presence of heterogeneous intensity distribution inside the prostate gland, and other imaging artifacts like speckle noise, shadow regions and low Signal to Noise Ratio (SNR). In this work, we propose to enhance the texture features of the prostate region using Local Binary Patterns (LBP) for the propagation of a shape and appearance based statistical model to segment the prostate in a multi-resolution framework. A parametric model of the propagating contour is derived from Principal Component Analysis (PCA) of the prior shape and texture information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve an optimal segmentation. The proposed method achieves a mean Dice Similarity Coefficient (DSC) value of 0.94±0.01 and a mean segmentation time of 0.6±0.02 seconds when validated with 70 TRUS images of 7 datasets in a leave-one-patient-out validation framework. Our method per- forms computationally efficient and accurate prostate segmentation in the presence of intensity heterogeneities and imaging artifacts

Statistical shape and texture model of quadrature phase information for prostate segmentation

International audiencePurpose: Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertro- phy and cancer. Computer-aided accurate and compu- tationally efficient prostate segmentation in TRUS im- ages is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications and heterogeneous in- tensity distribution in the prostate region. Method: A multi-resolution framework using texture features in a parametric deformable statistical model of shape and appearance was developed to segment the prostate. Local phase information of log-Gabor quadra- ture filter extracted texture of the prostate region in TRUS images. Large bandwidth of log-Gabor filter en- sures easy estimation of local orientations and zero re- sponse for a constant signal provides invariance to gray level shift. This aids in enhanced representation of the underlying texture information of the prostate unaf- fected by speckle noise and imaging artifacts. The para- metric model of the propagating contour is derived from principal component analysis of prior shape and texture information of the prostate from the training data. The Soumya Ghose*, Jhimli Mitra*, Arnau Oliver, Robert Mart'ı, Xavier Llad'o and Jordi Freixenet Computer Vision and Robotics Group, University of Girona Campus Montilivi, Edifici P-IV,17071 Girona, Spain. E-mail: [email protected], [email protected], {aoliver, marly, llado, and jordif}@eia.udg.edu Joan C.Vilanova Clinica Girona, Calle Joan Maragall 26, 17002 Girona, Spain. Josep Comet University Hospital Dr. Josep Trueta, Av. Frana, 17007 Girona, Spain. Fabrice Meriaudeau *Laboratoire Le2I - UMR CNRS 5158, Universit'e de Bour- gogne,12 Rue de la Fonderie, 71200 Le Creusot, Bourgogne, France. E-mail: [email protected]. parameters were modified using prior knowledge of the optimization space to achieve segmentation. Results: The proposed method achieves a mean Dice similarity coefficient value of 0.95±0.02, and mean ab- solute distance of 1.26±0.51 millimeter when validated with 24 TRUS images of 6 datasets in a leave-one- patient-out validation framework. Conclusions: The proposed method for prostate TRUS image segmentation is computationally efficient and pro- vides accurate prostate segmentations in presence of in- tensity heterogeneities and imaging artifacts