1 University of Rouen, LITIS EA4108, Rouen, France 2 Centre Henri-Becquerel, Rouen, France

1. 1University of Rouen, LITIS EA4108, Rouen, France 2Centre Henri-Becquerel, Rouen, France

2. Objective • Conventional radiotherapy (RT) treatment • “Fractionated” radiotherapy • total dose: 60-70 Gy • 2 Gy per fraction (day) • 5 fractions per week • Disappointing outcome • 12 months’ survival [*] • Variation of response among patients • in terms of tumor volume regression • Adapt the treatment individually • Iterative tumor segmentationduring the RT • Sequential medical images [*]: Dubray et al., in IRBM(2013)

3. Objective • To segment lung tumors in longitudinal PET images during radiotherapy • FDG-PET: metabolic activity baseline treatment

4. Challenge weak contrast baseline treatment small tumor CHALLENGE low spatial resolution 4x4x2 mm3

5. State of the art • Methods using a fixed threshold value, in terms of SUV (Standardized Uptake Value) • 40% of the SUVmax (T40, Ye et al., 2002) • Adaptive thresholding (TAD, Vauclin et al., 2009) • Advanced segmentation methods • statistics (FLAB, Hatt et al., 2009) • random walks (RW, Bagci et al., 2011) • ... SUV - La Valeur de fixation normalisée = (fixation dans le tissu d'intérêt)/((dose injectée)/(poids du patient))

6. Tumor Growth Model[*] • Advection Reaction Equation • tumor cell density • advection: describing the advective flux transport of tumor cells • proliferation: representing the tumor cell proliferation • treatment: quantifying the radiotherapeuticefficiency [*]: Mi et al., in IEEE Trans. Med. Imaging (2014)

7. Prediction • Prediction of tumor region at time

8. Proposed Segmentation Method • Random Walks (Grady, 2006) • Graph: nodes & edges • Probability belonging to the tumor D Intensity Similarity Input image

9. Proposed Segmentation Method • Random Walks (Grady, 2006) • Graph: nodes & edges • Probability belonging to the tumor • + prediction Intensity Similarity Input image

10. Solution • The matrix form: • Laplacian matrix, such that where , is the geometric distance of node iand j. Subscripts U: unlabeled nodes Subscripts L: labeled nodes [Onoma et al., 2012]

11. Definition of labeled nodes • Illustration ROI Label Seeds Input PET ROI boundary Non tumor seeds Define ROI Tumor seeds Prediction Dilate • Fuzzy C-Means: • low: C1 • moderate: C2 • high : C3 • Tumor Seeds: • intensity ≥ mean(C2, C3) Unlabeled nodes [Onoma et al., 2012]

12. Solution • Illustration ROI Label Seeds Solve Dirichlet Problem Input PET Probability map Define ROI Output Prediction Threshold Dilate Segmentation

13. Experimental Results Our Method • Data: • Three types of tumors: largeand smallsizes, heterogeneoustumors Input Expert RW T40 TAD FLAB large

14. Experimental Results Our Method Input Expert RW T40 TAD FLAB heterogeneous small

15. S E Experimental Results • Data: • 7 patients • 15 follow-up PETs Comparison of different methods (S) with the Expert (E) delineation spatial location volume

16. Experimental Results • Data: • 7 patients • 15 follow-up PETs Comparison of different methods (S) with the Expert (E) delineation

17. Tumor longitudinal Volumes PET Images Expert Our Method baseline treatment

18. Conclusion • Automatic tumor segmentation method • Follow up tumor response • Good performance for small tumor • Validation on larger patient images

19. Thank you !