Texture analysis in Medical Imaging: Applications in Cancer

Transcription

1 Texture analysis in Medical Imaging: in Cancer Angel Alberich-Bayarri, PhD 1 Biomedical Imaging Research Group (GIBI230) La Fe Polytechnics and University Hospital 2 QUIBIM SL

2 Outline to texture analysis Texture analysis techniques Texture analysis by 1st order, and fractals AI embedded texture analysis

3 Texture analysis attempts to quantify intuitive qualities described by terms such as rough, smooth, silky, or bumpy as a function of the spatial variation in pixel intensities. The roughness or bumpiness refers to variations in the intensity values, or gray levels. Texture analysis is used in a wide number of applications beyond medical imaging, including remote sensing, automated inspection, self-driving cars. Widely accepted definition of texture analysis Analysis of the spatial variation of pixel intensities

4 The concept of order (i.e. first order, second order, ) Number of steps required to extract the relevant quantitative information

5 Discriminating between image textures with the naked eye depends largely upon the difference in the second-order statistics of the textures Same first order statistics Different second order statistics Same first order statistics Same second order statistics The human visual system cannot discriminate well between texture pairs with matching second order statistics. Julesz, 1975

6 What about units? Most texture features do not have units: arbitrary units are used

7 for Texture Analysis 1. First-order statistical texture analysis 2. Second-order texture analysis through Gray-Level Coocurrence Matrices () 3. Fractal Texture Analysis 4. Higher-order statistical texture analysis through GrayLevel Run-Length (GLRL) 5. Fourier Power Spectrum

8 for Texture Analysis 1. First-order statistical texture analysis 2. Second-order texture analysis through Gray-Level Coocurrence Matrices () 3. Fractal Texture Analysis 4. Higher-order statistical texture analysis through GrayLevel Run-Length (GLRL) 5. Fourier Power Spectrum

9 First order, and Fractals First order: histogram analysis Simple and intuitive No consideration on spatial relationship between pixels Mean Median Variance Skewness Kurtosis Energy Entropy Histogram texture features

10 First order, and Fractals Second order: Gray-Level Co-ocurrence Matrix Not intuitive Considers spatial relationship between pixels

11 First order, and Fractals Second order: Gray-Level Co-ocurrence Matrix Not intuitive Considers spatial relationship between pixels Our simple image with 4 grayscale levels Co-ocurrence matrix at 0º

12 First order, and Fractals Second order: Gray-Level Co-ocurrence Matrix Contrast Correlation Energy Entropy Homogeneity Information Probability Prominence of cluster 9. Variance Co-ocurrence matrix generation Texture features (more than 100 described, several provide similar information)

13 First order, and Fractals Fractal analysis Fractal dimension allows to quantify the degree of irregularity of tissue components log( N ) D 2 D log( ) k

14 Artificial Intelligence Deep Learning for Medical Imaging Convolutional Neural Networks (CNN), the technological basis of Deep Learning intrinsically perform massive texture analysis Courtesy of NVIDIA

15 Artificial Intelligence Deep Learning for Medical Imaging Convolutional Neural Networks (CNN), the technological basis of Deep Learning intrinsically perform massive texture analysis Textons extracted

16 Application 1: Rectal Cancer Study on consolidation therapy RT+Chemo vs. Long cycle CRT in rectal cancer MR protocol: HR-FSE-T2, no DWI nor DCE for minimizing imaging variability across centers

17 Application 1: Rectal Cancer Study on consolidation therapy RT+Chemo vs. Long cycle CRT in rectal cancer MR protocol: HR-FSE-T2, no DWI nor DCE for minimizing imaging variability across centers Baseline Follow-up

18 Application 1: Rectal Cancer Textural features Patient # Example on subjects grouping according to their texture signature

19 Application 1: Rectal Cancer Study on consolidation therapy RT+Chemo vs. Long cycle CRT in rectal cancer MR protocol: HR-FSE-T2, no DWI nor DCE for minimizing imaging variability across centers

20 Application 1: Rectal Cancer Study on consolidation therapy RT+Chemo vs. Long cycle CRT in rectal cancer Prognostic value evaluation: Texture features at baseline D Fractal Dimension D Fractal Dimension vs. Relapse of Rectal Cancer 0 1 Relapse

21 Application 1: Rectal Cancer Study on consolidation therapy RT+Chemo vs. Long cycle CRT in rectal cancer Evaluation of hallmarks: Texture features vs. invasion % Change D2D vs. Invasion 5 0 % Change of D2D Entropy Entropy vs. Invasion in Rectal Cancer 0 1 Invasion 0 1 Invasion

22 Application 2: Lymphoma PET/CT Beyond SUVmax Prognostic value for Progression Free Survival Treatment Response evaluation in Follicular Lymphoma Lesional mask extraction by 41% SUVmax criteria1 *Boellaard R. EANM 2010

23 Application 2: Lymphoma PET/CT Beyond SUVmax Prognostic value for Progression Free Survival Treatment Response evaluation in Follicular Lymphoma

24 Application 2: Lymphoma PET/CT Beyond SUVmax Prognostic value for Progression Free Survival Treatment Response evaluation in Follicular Lymphoma Treatment response histogram analysis

25 Application 2: Lymphoma PET/CT Beyond SUVmax Prognostic value for Progression Free Survival Treatment Response evaluation in Follicular Lymphoma Heterogeneity in metabolic avidity of lesions (Metabolic Heterogeneity, MH) can be explained by several factors: Kurtosis Cellularity Necrosis Hypoxia Fibrosis Low MH Kurtosis High MH

26 GIBI-QUIBIM team

27 Texture analysis in Medical Imaging: in Cancer Angel Alberich-Bayarri, PhD 1 Biomedical Imaging Research Group (GIBI230) La Fe Polytechnics and University Hospital 2 QUIBIM SL