Tumor Discrimination Using Textures 2 Presented by: Maysam Heydari

Introduction Main goal: Discrimination between different tumor grades/types using textural properties Tumor pathologies: –Grade 2: astro (7), oligo (22) –Grade 3: aa (2), ao (1), amoa (1) –Grade 4: gbm (17)

Method 0 For each patient, T1, T1C, and T2 histograms constructed over all the tumor pixels (texture intensities) over all slices. Histograms normalized and ranges adjusted over all tumors.

Method 0 Each patient’s tumor is represented by a histogram for each modality and texture feature. The histograms are used as vector inputs to kmeans (k = 2) clustering.

Results 0 lowgrade/highgrade: mismatch rates T1 T1C T2 Raw st MR nd MR rd MR th MR th MR th MR th MR th MR Energy Contrast BGLAM

Results 0 gbm/rest: mismatch rates T1 T1C T2 Raw st MR nd MR rd MR th MR th MR th MR th MR th MR Energy Contrast BGLAM

Conclusions 0 Gaussian and LoG filter features cluster (k = 2) well with few mismatches on T2. BGLAM symmetrical similarity features cluster (k = 2) well on T1C. Oligos are prominent among the mismatches.

Method 0.1) Compare the grouping of oligos (22) and gbms(17) in the kmeans clusters for k = 2, 3, 4. How well are oligos grouped together? How well are gbms grouped together ? –Consider clusters that contain 60% or more of tumors of a certain type: oligo or gbm.

Results 0.1) oligo gbm T1 T1C T2 T1 T1C T Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM k = 2

Results 0.1) oligo gbm T1 T1C T2 T1 T1C T Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM k = 3

Results 0.1) oligo gbm T1 T1C T2 T1 T1C T Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM k = 4

Conclusions 0.1) gbms cluster well on T1C. oligos cluser very poorly on T1C and generally poorly on T1 and T2.

Method 1 Run SVM with linear kernel on the histograms as vectors. Train on the 50 tumors and test on the 50 tumors (training error). 3fold validation … (test error). Classes: –lowgrade (astro2, oligo) vs. highgrade (aa, ao, amoa, gbm) –gbm vs. the rest

Training misclassification errors (%) T1 T1C T Results 1 Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM 3-fold testing misclassification errors (%) T1 T1C T Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM l owgrade/highgrade std<12 and mean<35

Training misclassification errors (%) T1 T1C T Results 1 Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM 3-fold testing misclassification errors (%) T1 T1C T Raw 1st MR8 2nd MR8 3rd MR8 4th MR8 5th MR8 6th MR8 7th MR8 8th MR8 Energy Contrast Correlation BGLAM gbm/rest std<12 and mean<35

Conclusions 1 Reiterates: –gbm discriminates well on T1C. –Gaussian and LoG good discriminating textural features. –BGLAM symmetric similarities discriminate well on T1C.

Method 2 Combine all texture features: –For each tumor, use mean and std. –Combine mean and std values of all texture features for each tumor. On the combined texture features: –2.1) Run kmeans (k = 2) –2.2) Run SVM with linear kernel Classes: –lowgrade (astro2, oligo) vs. highgrade (aa, ao, amoa, gbm) –gbm vs. the rest

Results 2.1) Clustering mismatch errors (%) lowgrade/highgrade : T1 T1C T gbm/rest T1 T1C T oligo gbm T1 T1C T2 T1 T1C T k = 2 k = 3 k = 4 Any clusters that contain 60% or more of tumors of a certain type: oligo or gbm.

Results 2.2) Training misclassification errors (%) T1 T1C T lowgrade/highgrade : Training misclassification errors (%) T1 T1C T gbm/rest 3-fold testing misclassification errors (%) T1 T1C T fold testing misclassification errors (%) T1 T1C T std<12 and mean<35

What’s Next? Analyze mismatches and misclassified tumors more closely. Test SVM on new data? BGLAM features use symmetry … incorporate symmetry with other features? Ignore 3rd grade tumors (aa, ao, amoa) and only look at grade 2 (astro2, oligo) vs. gbms? More thorough svm testing? Need more gbms? …