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Frederic Sampedro; Anna Domenech; Sergio Escalera |
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Title |
Obtaining quantitative global tumoral state indicators based on whole-body PET/CT scans: A breast cancer case study |
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Journal Article |
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Year |
2014 |
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Nuclear Medicine Communications |
Abbreviated Journal |
NMC |
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35 |
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4 |
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362-371 |
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Abstract |
Objectives: In this work we address the need for the computation of quantitative global tumoral state indicators from oncological whole-body PET/computed tomography scans. The combination of such indicators with other oncological information such as tumor markers or biopsy results would prove useful in oncological decision-making scenarios.
Materials and methods: From an ordering of 100 breast cancer patients on the basis of oncological state through visual analysis by a consensus of nuclear medicine specialists, a set of numerical indicators computed from image analysis of the PET/computed tomography scan is presented, which attempts to summarize a patient’s oncological state in a quantitative manner taking into consideration the total tumor volume, aggressiveness, and spread.
Results: Results obtained by comparative analysis of the proposed indicators with respect to the experts’ evaluation show up to 87% Pearson’s correlation coefficient when providing expert-guided PET metabolic tumor volume segmentation and 64% correlation when using completely automatic image analysis techniques.
Conclusion: Global quantitative tumor information obtained by whole-body PET/CT image analysis can prove useful in clinical nuclear medicine settings and oncological decision-making scenarios. The completely automatic computation of such indicators would improve its impact as time efficiency and specialist independence would be achieved. |
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HuPBA;MILAB |
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SDE2014a |
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2444 |
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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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Journal Article |
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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 |
Frederic Sampedro; Anna Domenech; Sergio Escalera |
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Title |
Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans |
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Journal Article |
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2014 |
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Journal of Medical Imaging and Health Informatics |
Abbreviated Journal |
JMIHI |
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4 |
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6 |
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825-831 |
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CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION |
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In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans. |
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HuPBA;MILAB |
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Admin @ si @ SDE2014b |
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2548 |
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Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
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Title |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
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Year |
2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
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28 |
Issue |
1 |
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87-101 |
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Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
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The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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0921-7126 |
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HUPBA;MILAB |
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no |
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Admin @ si @ CCE2015 |
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2549 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Puig |
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Title |
Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
46 |
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1-10 |
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Keywords |
Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation |
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In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. |
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HuPBA;MILAB |
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Admin @ si @ SEP2014 |
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2550 |
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