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| Author | Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio | ||||
| Title | Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source | Type | Journal Article | ||
| Year | 2015 | Publication | Journal of Medical Imaging and Health Informatics | Abbreviated Journal | JMIHI |
| Volume | 5 | Issue | 2 | Pages | 192-201 |
| Keywords | CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY | ||||
| Abstract | 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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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SED2015 | Serial | 2584 | ||
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| Author | Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio | ||||
| Title | A computational framework for cancer response assessment based on oncological PET-CT scans | Type | Journal Article | ||
| Year | 2014 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
| Volume | 55 | Issue | Pages | 92–99 | |
| Keywords | Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis | ||||
| Abstract | 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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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SED2014 | Serial | 2606 | ||
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