Modeling and preoperative planning for kidney surgery

Transcription

1 Modeling and preoperative planning for kidney surgery Refael Vivanti Computer Aided Surgery and Medical Image Processing Lab Hebrew University of Jerusalem, Israel Advisor: Prof. Leo Joskowicz Clinical advisor: Dr Yoav Minz

2 Kidney Cancer 54,390 new cases and 13,010 deaths in the US (2008) Often do not respond to chemotherapy or radiotherapy Preferred treatment: partial Nephrectomy

3 MIS partial Nephrectomy MIS - Surgery through key-holes Controlled by video Temporary Ischemia Reason: to avoid bleeding during the surgery. Restricts surgery time to 25 minutes before organ damage.

4 Preoperative planning Currently: The surgeon browse through 4 scans. ~40 minutes process. Our goal: Preoperative planning system Accuracy Ease of use Clinical relevance

5 Model vs. Visualization Visualization Model You do the interpretation. You fill/omit missing info. Limited measurements. Computer interprets. Explicit delineation. Spatial and volumetric measurements. Our algorithmic goal : components modeling

6 Components: Kidney Arteries Veins Collecting system (ureter) Kidney Anatomy Vessels diameter: 0.5-5mm

7 Related work No work on kidney vessels. General vessels segmentation: Pattern recognition methods Tubularity filters (Sato et al, Frangi et al) Region growing (Schmitt et al ) Model-based methods active contour deformable physical models Tracking-based methods Track centerlines optimum path in a graph

8 Input - 4 Phase CT Basic phase Arterial phase Venous phase Ureter phase

9 Registration between phases On tight ROI Grade: Mutual Information Affine registration model Ureter phase Arterial phase

10 Vessels Segmentation Problem: not enough arterial voxels for distinct Gaussian in 3D scan. Arteries Arteries

11 Volume rendering Solution: 1. Max-value Volume rendering max 2. Expectation- Maximization Arteries

12 Seg. by volume rendering 3. 2D segmentation: adaptive threshold Remove small connected components ) ( ) ( ) ( ) ( threshold x threshold x x x a b G b P a b G b P G a P G a P G2 G1

13 Seg. by volume rendering 4. Back projection to 3D: Only on segmented pixels Using arg-max operator Create seeds for next stage. Arg-max

14 Seg. by volume rendering Problem: Occlusions Solution: Repeat for 12 views

15 Seg. by volume rendering 5. Frangi vessel enhancement filter 1 In the Hessian, a vessel has:, 1 2, 3 Similarity to blob-like structure: Deviation from a plate-like structure: Hessian norm 1. Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhancement filtering

16 Seg. by volume rendering 6. Region growing Input: Seeds from stage 3 2 CT scans: - artery phase - base phase 4 parameters for each voxel: - Value from artery phase - Value from base phase - Frangi grade in artery phase - Frangi grade in base phase

17 Seg. by volume rendering 6. Region growing algorithm: In each iteration: - Calculate 4D and on current segmented area. - Dilute the segmented area. - Segment as artery voxel x which holds 3 < x < 3 - Remove small connected components. Stop criterion: diameter of the largest artery.

18 Seg. by mutual distribution For large components (kidney, aorta) 1. Calculate mutual histogram between relevant phase and registered base phase. 2. Run Expectation-Maximization. 3. Find relevant (off-diagonal) Gaussian. 4. Find thresholds between the relevant Gaussian and its neighbors. 5. Segment by the thresholds. 6. Choose largest connected component.

19 Experimental results Datasets: 4-phase CT scans of 3 patients. Ground truth approved by a radiologist. Comparison metrics: Volumetric Overlap Relative difference Surface distance Mean RMS Max

20 Experimental results - kidney

21 Experimental results Kidney border Overlap Error. [%] Volume Diff. [%] Avg. Dist. [mm] RMS Dist. [mm] Max. Dist. [mm] ET DW DM Average Small overlap error between volumes

22 Experimental results - ureter

23 Experimental results Ureter Overlap Error. [%] Volume Diff. [%] Avg. Dist. [mm] RMS Dist. [mm] Max. Dist. [mm] ET DW DM Average

24 Experimental results - arteries

25 Experimental results Arterie s Overlap Error. [%] Volume Diff. [%] Avg. Dist. [mm] RMS Dist. [mm] Max. Dist. ET DW DM Average Small distance between meshes [mm]

26 Experimental results - veins

27 Experimental results Veins Overlap Volume Avg. Dist. RMS Dist. Max. Dist. [mm] [mm] [mm] Error. [%] Diff. [%] ET DW DM Average

28 Experimental results - combined

29 Segmentation by volume rendering back projection Contributions Segmentation by mutual distribution Preoperative planning system.

30 Discussion Some components are segmented better than others. Overlap error: 1.7% in kidney and 25.65% in blood vessels. Thin vessels segments are lost in the noise reduction process. Bad registration out of the kidney. Division of the ureter into components: The contrast agent is not present in the connections.

31 Thank you!

32 Frangi vessel-enhancement Deviation of a plate-like structure: Similarity to blob-like structure: Frobenius norm, second-order-like structure: Frangi uses = 0.5, = 0.5, c = 0.25 of the max intensity.