Multicriteria 3D PET image segmentation Application to oncology and - PowerPoint PPT Presentation

Introduction Methodology Experimental study Future works Multicriteria 3D PET image segmentation Application to oncology and cancer lesion extraction Francisco Javier Alvarez Padilla, Éloïse Grossiord, Barbara Romaniuk, Benoît Naegel, Camille Kurtz, Hugues Talbot, Laurent Najman, Romain Guillemot, Dimitri Papathanassiou, Nicolas Passat IPTA 2015 – Orléans (France) November 5, 2015 1 / 19

Introduction Methodology Experimental study Future works Outline 1 Introduction Methodology: Multicriteria 3D PET image segmentation 2 Experimental study on oncology and cancer lesion extraction 3 4 Future works 2 / 19

Introduction Methodology Experimental study Future works Outline 1 Introduction Methodology: Multicriteria 3D PET image segmentation 2 Experimental study on oncology and cancer lesion extraction 3 4 Future works 2 / 19

Introduction Methodology Experimental study Future works Big Picture: Medical imaging & Image analysis Image interpretation: Diagnosis decision making The number of medical images is exploding ( > 200 images per exam) Substantial variations in interpretation between physicians Largely an unassisted process Medical image interpretation is subjective and accuracy varies widely Objective: Integrate computer-based assistance To detect / segment / classify anatomical and pathological structures of interest (lesions, organs, etc.) in medical images To help physicians to standardize the interpretation of medical images 3 / 19

Introduction Methodology Experimental study Future works Focus: Oncology & Nuclear medicine PET-CT imaging Positron Emission Tomography (PET) constitutes the gold-standard for image-based diagnosis and patient follow-up for cancers PET scans use radiopharmaceuticals (FDG, FNa) to create images of active blood flow, informing about the metabolic activity of lesions In contrast to other 3D imaging modalities (MRI, CT), PET images have : low spatial resolution acquisition / reconstruction / anatomical artifacts (left) PET image and (right) CT image 4 / 19

Introduction Methodology Experimental study Future works Focus: Oncology & Nuclear medicine PET-CT imaging Positron Emission Tomography (PET) constitutes the gold-standard for image-based diagnosis and patient follow-up for cancers PET scans use radiopharmaceuticals (FDG, FNa) to create images of active blood flow, informing about the metabolic activity of lesions In contrast to other 3D imaging modalities (MRI, CT), PET images have : low spatial resolution acquisition / reconstruction / anatomical artifacts (left) PET image and (right) CT image 4 / 19

Introduction Methodology Experimental study Future works Focus: Oncology & Nuclear medicine Analysis of lesions from PET images PET images are mostly processed via basic approaches , such as fixed / adaptive thresholding The Standardized Uptake Value (SUV) constitutes the gold-standard, despite its limitations [Buvat, 2007] Segmentation strategies recently emerged for detecting / extracting the lesions rely on intensity-based approaches ( e.g. , thresholding, region-growing) can lead to inaccurate results where lesions are mixed-up with hyperfixating organs Other approaches also intend to embed additional information ( e.g. , shape priors, anatomical context) � In all these strategies, the priors are limited in ROI generated by a number , defined beforehand and considered a priori as threshold based algorithm in correct, thus constituting hard parameters a 3D PET image 5 / 19

Introduction Methodology Experimental study Future works Focus: Oncology & Nuclear medicine Analysis of lesions from PET images PET images are mostly processed via basic approaches , such as fixed / adaptive thresholding The Standardized Uptake Value (SUV) constitutes the gold-standard, despite its limitations [Buvat, 2007] Segmentation strategies recently emerged for detecting / extracting the lesions rely on intensity-based approaches ( e.g. , thresholding, region-growing) can lead to inaccurate results where lesions are mixed-up with hyperfixating organs Other approaches also intend to embed additional information ( e.g. , shape priors, anatomical context) � In all these strategies, the priors are limited in ROI generated by a number , defined beforehand and considered a priori as threshold based algorithm in correct, thus constituting hard parameters a 3D PET image 5 / 19

Introduction Methodology Experimental study Future works Purpose Hypothesis There does not exist any consensus related to the most relevant criteria (features) for segmenting lesions in PET images Purpose To learn the expert knowledge carried by their behaviour when analysing 3D PET images , to identify an arbitrary number of relevant quantitative criteria To use this knowledge to reproduce the expert behaviour in interactive and robust lesion segmentation strategies Proposed method: Multicriteria 3D PET image segmentation Step 1. Knowledge extraction from examples Classification approach relying on example-based learning strategies, allowing for interactive example definition and more generally incremental refinement Step 2. Knowledge-based segmentation Segmentation approach relying on efficient (morphological) hierarchical segmentation, allowing vectorial attribute handling 6 / 19

Introduction Methodology Experimental study Future works Outline 1 Introduction Methodology: Multicriteria 3D PET image segmentation 2 Experimental study on oncology and cancer lesion extraction 3 4 Future works 6 / 19

Introduction Methodology Experimental study Future works Outline 2 Methodology: Multicriteria 3D PET image segmentation Knowledge extraction from examples Knowledge-based segmentation 6 / 19

Introduction Methodology Experimental study Future works Knowledge extraction step Objective: To automatically learn, from examples defined in PET 3D volumes, discriminative imaging criteria to guide the segmentation to extract lesions Building a predictive / classification model Step 1. Build a learning database To allow the experts to select positive (lesions) and negative (hyperfixating organs) examples in PET images This task is carried out by experts using an interactive 3D stereoscopic visualization approach based on multimodal (PET-CT) imaging Step 2. Train a classifier To extract from this learning database what are the most relevant imaging criteria (features) to separate lesions from hyperfixating organs This task relies on supervised classification (Decision Trees – C4.5) to build a 3-class model: lesions, hyperfixating organs & other imaging areas 7 / 19

Introduction Methodology Experimental study Future works Knowledge extraction step Objective: To automatically learn, from examples defined in PET 3D volumes, discriminative imaging criteria to guide the segmentation to extract lesions Building a predictive / classification model Step 1. Build a learning database To allow the experts to select positive (lesions) and negative (hyperfixating organs) examples in PET images This task is carried out by experts using an interactive 3D stereoscopic visualization approach based on multimodal (PET-CT) imaging Step 2. Train a classifier To extract from this learning database what are the most relevant imaging criteria (features) to separate lesions from hyperfixating organs This task relies on supervised classification (Decision Trees – C4.5) to build a 3-class model: lesions, hyperfixating organs & other imaging areas 7 / 19

Introduction Methodology Experimental study Future works Knowledge extraction step Objective: To automatically learn, from examples defined in PET 3D volumes, discriminative imaging criteria to guide the segmentation to extract lesions Building a predictive / classification model Step 1. Build a learning database To allow the experts to select positive (lesions) and negative (hyperfixating organs) examples in PET images This task is carried out by experts using an interactive 3D stereoscopic visualization approach based on multimodal (PET-CT) imaging Step 2. Train a classifier To extract from this learning database what are the most relevant imaging criteria (features) to separate lesions from hyperfixating organs This task relies on supervised classification (Decision Trees – C4.5) to build a 3-class model: lesions, hyperfixating organs & other imaging areas 7 / 19

Introduction Methodology Experimental study Future works Knowledge extraction step Objective: To automatically learn, from examples defined in PET 3D volumes, discriminative imaging criteria to guide the segmentation to extract lesions Building a predictive / classification model Step 1. Build a learning database To allow the experts to select positive (lesions) and negative (hyperfixating organs) examples in PET images This task is carried out by experts using an interactive 3D stereoscopic visualization approach based on multimodal (PET-CT) imaging Step 2. Train a classifier To extract from this learning database what are the most relevant imaging criteria (features) to separate lesions from hyperfixating organs This task relies on supervised classification (Decision Trees – C4.5) to build a 3-class model: lesions, hyperfixating organs & other imaging areas The classification model is used in the segmentation step to select ROIs from PET image segmentation results that could correspond to lesions for cancer detection 7 / 19

Introduction Methodology Experimental study Future works Outline 2 Methodology: Multicriteria 3D PET image segmentation Knowledge extraction from examples Knowledge-based segmentation 7 / 19