Variability of LIDC Panel Segmentations and Soft Segmentation of Lung Nodules

1. Variability of LIDC Panel Segmentations and Soft Segmentation of Lung Nodules Presented by Stephen Siena and Olga Zinoveva

2. Overview Discussion of LIDC data New variability metric Soft segmentation Related work Methods Discussion Future Work

3. The LIDC Chest CT scans reviewed by 4 radiologists Semantic characteristics and contours Benefits to research Access for everyone Sets standard Problem: No ground truth No perfect detection/outline of nodules

4. Our Proposed Solution Find “accuracy” of radiologists first Provides measure of panel segmentations’ consistency Validation of reference truth Incorporates and improves upon previous metrics

5. Methodology • Cost matrix • Variability matrix

6. Methodology Variability index Normalized variability index

7. Radiologist outlines • Pmap • Cost matrix (R = 4; k = 10) • (d-g) Variability matrix after0, 1, 2, 3 iterations • Final variability matrix • VI = 60; VIn = 5.1064

8. Results VIn = .5227 VIn = 1.8198 VIn = 3.2339 VIn = 7.0345 VIn = 15.1705 VIn = 37.8774

9. Complements Overlap Overlap = .2245VIn = 11.6000 Overlap = .2246VIn = 35.4449 Overlap = .2064VIn = 81.4449 Overlap = .6771VIn = 12.5896 Overlap = .2763VIn = 12.3711 Overlap = .4462VIn = 12.5101

10. Background • Most lung nodule segmentation algorithms produce hard segmentations • Probabilistic segmentations used for other medical imaging purposes • Cai, Hongmin et al. Produced brain segmentations • Tang, Hui – kidney segmentations • van Ginneken, Bram produced lung nodule segmentations on the first LIDC dataset

11. Dataset and pre-processing • All slices from the LIDC 85 dataset that contain four radiologist contours • 264 slices representing 39 nodules • Different CT scanners convert HU to intensity differently • Solution – intensity shift based on the rescale intercept

12. Random point selection • Points selected proportionately from every region and every image. 0%, 25%, 50%, 75%, 100%

13. Random point selection • Coordinates selected randomly, but must be at least R pixels away from each other for any region PinT is the total number of pixels in agreement area i of image n Pins is the number of pixels selected from agreement area i of image n

14. Classifier • Intensity and texture (Gabor and Markov) features calculated for a 9X9 neighborhood around each pixel • Classifier assigned a continuous probability (0-100) for each pixel’s membership in the nodule class • These values were thresholded to produce a p-map of the segmentation

15. Classifier results • Median soft overlap: 0.53 • Median VI: 4.24 (Q1:2.5, Q2:9.8) • Chest wall causes the majority of errors • Over-segmentation on most slices

16. Classifier results Nodule Radiologist p-map Classifier’s p-map

17. Post-processing: VI Trimming Terminate

18. VI Trimming: Example

19. Results after VI trimming • Median soft overlap: 0.57 (vs. 0.53) • Median RAE: 9.9 (vs. 17.5)

20. Results after VI trimming

21. Ongoing and future work • Improve segmentation of nodules attached to chest walls • Select seed points without manual input • Calculate VI for the segmentations • Work with all slices for the 39 nodules • Expand to the new LIDC dataset • Segment lungs • Eliminates the need for chest wall separation algorithms • Allows for better intensity normalization

22. References • Cai, Hongmin et al. “Probabilistic Segmentation of Brain Tumors Based on Multi-Modality Magnetic Resonance Images,” 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 600-603 (April 2007) • Tang, Hui et al. “A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model,” Computerized Medical Imaging and Graphics (August 2009) • van Ginneken, Bram, “Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans,” MICCAI 2006 Proceedings, Part II pp. 912-919 (October 2006)

23. Questions?