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Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
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A computational framework for cancer response assessment based on oncological PET-CT scans |
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2014 |
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Computers in Biology and Medicine |
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CBM |
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55 |
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92–99 |
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Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis |
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In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks. |
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HuPBA;MILAB |
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Admin @ si @ SED2014 |
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2606 |
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Author |
Mariella Dimiccoli |
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Title |
Fundamentals of cone regression |
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2016 |
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Journal of Statistics Surveys |
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10 |
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53-99 |
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cone regression; linear complementarity problems; proximal operators. |
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Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution. |
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1935-7516 |
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MILAB; |
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Admin @ si @Dim2016a |
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2783 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
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Title |
Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source |
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2015 |
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Journal of Medical Imaging and Health Informatics |
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JMIHI |
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5 |
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2 |
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192-201 |
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CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY |
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Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level. |
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HuPBA;MILAB |
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Admin @ si @ SED2015 |
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2584 |
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Author |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
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Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
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2016 |
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International Journal of Computer Vision |
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IJCV |
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118 |
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1 |
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49–64 |
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Contextual rescoring; Poselets; Human pose estimation |
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In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. |
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Springer US |
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0920-5691 |
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HuPBA;MILAB; |
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Admin @ si @ HES2016 |
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2719 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Extending anisotropic operators to recover smooth shapes |
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Year |
2005 |
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Computer Vision and Image Understanding |
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99 |
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1 |
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110-125 |
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Contour completion; Functional extension; Differential operators; Riemmanian manifolds; Snake segmentation |
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Anisotropic differential operators are widely used in image enhancement processes. Recently, their property of smoothly extending functions to the whole image domain has begun to be exploited. Strong ellipticity of differential operators is a requirement that ensures existence of a unique solution. This condition is too restrictive for operators designed to extend image level sets: their own functionality implies that they should restrict to some vector field. The diffusion tensor that defines the diffusion operator links anisotropic processes with Riemmanian manifolds. In this context, degeneracy implies restricting diffusion to the varieties generated by the vector fields of positive eigenvalues, provided that an integrability condition is satisfied. We will use that any smooth vector field fulfills this integrability requirement to design line connection algorithms for contour completion. As application we present a segmenting strategy that assures convergent snakes whatever the geometry of the object to be modelled is. |
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1077-3142 |
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IAM;MILAB |
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IAM @ iam @ GIR2005 |
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1530 |
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