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Σπύρος Γιάγκος
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11 Known causes: Drugs Collagen Vascular Exposures Genetict IIPs: Idiopathic interstitial pneumonias Granulomatous lung diseases: Sarcoidosis Fungal Mycobacterial Unique entities: Pulmonary Alveolar Proteinosis Eosinophilic Granulomatosis Eosinophilic Pneumonia Lymphangioleiomyomatosis Capillaritis IPF: idiopathic pulmonary fibrosis 55 % 25 % NSIP: nonspecific interstitial pneumonia RB-ILD: respiratory bronchiolitis interstitial lung disease % <2% AIP: acute interstitial pneumonia DIP: desquamative interstitial pneumonia 5% COP: cryptogenic organising pneumonia LIP: lymphocytic interstitial pneumonia <1%

13 CT Scan Lung Segmentation Lung Field Segmentation Bronchovascular Tree Segmentation ILD Pa ern Classification Feature Extraction Feature Selection Classification ILD antification ILD map Findings, Extend, Localization Diagnosis Rule Based Scheme Machine Learning Clinical Parameters

16 R i,j i j

17 C i,j i j 2 8

18 D n D D

19 n k k n

20 X = [x 1, x 2,, x n ] C j C = [c 1, c 2,, c k ] p(c j x 1, x 2,, x n ) p(x 1, x 2,, x n C j )p(c j ) p(c j x 1, x 2,, x n ) X C j n p(x 1, x 2,, x n C j ) p(x k C j ) n p(c j x 1, x 2,, x n ) p(c j ) p(x k C j ) X C j k=1 k=1 x n b y( x) = f(w T x) = f( n i=1 W ix i + b) f : R R W W y( x) x

21 X 1 H 1 X 2 Y 1 X 3 H n Y n X n ϕ

22 φ S S {h(x, Θ k ), k = 1,... } {Θ k }

25 0 u, v < 4 [0, 1] I(x, y) F (u, v) F (u, v) = α(u)α(v) N 1 N 1 x=0 y=0 I(x, y) [ π(2x + 1)u π(2y + 1)v ] [ ] 2N 2N

26 u, v = 0, 1,..., N 1 α 1/N x = 0 α(x) = 1/N x 0 I(x, y) = N 1 N 1 u=0 v=0 α(u)α(v)f (u, v) [ π(2u + 1)x π(2v + 1)y ] [ ] 2N 2N F (u, v) I(x, y) α(u)α(v) [ π(2u + 1)x π(2v + 1)y ] [ ] 2N 2N u, v < 4 f uv (x, y) u, v [1, N 1] N N 0 u, v < N 1 {I u,v (x, y)} I(x, y) X F X (x) = (X x) X x x

27 Convolute original image with filter bank Feature vector Original image Filtered images q-quantiles X1 X2 Filter bank Intensity histogram Xn F 1 (y), y [0, 1] x F (x) = y q q 1 k 0 < k < q D = (N N) q + b q b [0, 1] 5 5

29 n n n

30 n

31 Feature Vector Fixed Scale Classifier RF classifier model Classifier s votes V1 V2 Classifier s votes in Patches V 2 V 1 V 2 V 1 V n Vn

32 Σχήμα 3.5: Παραδείγματα ταξινόμησης πνευμονικού παρεγχύματος. Από αριστερά προς τα δεξιά: μέρη αξονικής τομογραφίας, ενδείξεις προτύπου αναφοράς, πρόβλεψη του υπολογιστή. Από πάνω προς τα κάτω: φυσιολογικό (κόκκινο),εσμυρισμένο (κίτρινο), κυψελωτό (μπλε), ενοποιημένο (πράσινο), δικτυωτό (κυανό), δικτυωτό/εσμυρισμένο (μοβ). 23

34 increased attenuation tuberculosis early fibrosis peripheral micronodules cysts macronodules bronchiectasis pcp emphysema fibrosis reticulation healthy consolidation ground glass micronodules 1mm

35 1 2mm 10 15mm 1 2s mm ±

36 db_root ILD_DB_lungMasks Case_id lung_mask lung-mask-0001.dcm CT-0001.dcm ILD_DB_volumeROIs Case_id roi_mask roi-mask-0001.dcm CT-0001.dcm ILD_DB_txtROIs Case_id CT-txtROIs.txt CT-0001.dcm case_id: '109' label: 'ground_glass' localisation: basal slice_number: 11 xvalues: [1x134 double] yvalues: [1x134 double] spacing_x:

Study: 1.2.124.113532.129.195.3.60.20030403.100010 Series: 1.2.840.113704.1.111.154.1049453263.6 SpacingX: 0.68359375 SpacingY: 0.68359375 SpacingZ: 10.

37 Study: Series: SpacingX: SpacingY: SpacingZ: 10.0 label: ground_glass localisation: basal slice_number: 9 nb_points_on_contour: spacing_y: ct_scan: [1x83 char] lung_mask: [1x99 char] roi_mask: [1x98 char]

39 initial database new database re-structure patch extraction patches dataset database browser tool Talisman db_browser GUI re-annotate MAT-file F avg = 1 M M F c c M c=1 F c F c = 2 precision c recall c precision c recall c recall c = samples correctly classified as c samples of class c precisson c = samples correctly classified as c samples classified as c

40 Features PIXLV GLDM HIST QUANT GLRLM GLCM LBP F-score (%) F avg ( )

41 F avg ( ) 3 3 k = 10 k = 10

42 F avg ( ) F avg ( ) F avg ( )

44 F-score Forward SFS Backward SFS no SFS number of feature selection steps

45 5 5

68 Properties: db_root: 'C:\{$db_root}' directories: [120x1 struct] cases: [120x1 struct] rois: [2084x1 struct] Methods: setdbroot: sets the db_root property getalldirectories: scan db_root for all subdirectories and store them at directories property loadrois: loads all ROIs at rois property loadcases: loads all CASEs at cases property filterrois: filters ROIs using pattern labels filtercases: filters CASEs using pattern labels createpath: return the actual path of ct scans by combining the db_root with [directories] id: {Current case id} age: {Age of patient} gender: {Gender of patient} rois: [1x3 struct] directories: [1x1 struct]

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