A New GPU-Based Level Set Method for Medical Image Segmentation

Transcription

1 A New GPU-Based Level Set Method for Medical Image Segmentation Wenzhe Xue Research Assistant Radiology Department Mayo Clinic, Scottsdale, AZ Ph.D. Student Biomedical Informatics Arizona State University, Tempe, AZ 2014 MFMER slide-1

2 1. Cancer and Tumor Volume 2. GPU Level Set Method 3. Other Medical Applications 2014 MFMER slide-2

3 MFMER slide-3

4 Cancer 2nd leading cause of death worldwide In the United States, million new cancer cases 580,000 cancer deaths Average cost to develop a new drug in 2005 $1.3 billion 2014 MFMER slide-4

5 Cancer Treatment response is associated with tumor shrinkage CR PR Stable Disease PD 100% Decrease 30% 0 20% Increase Complete Response (CR): 100% decrease Partial Response (PR): >30% decrease Progressive Disease (PD): >20% increase Stable Disease (SD): none of the above 2014 MFMER slide-5

6 Diameter-based methods: WHO or RECIST Lesion size Diameters: Precise Accurate Rapid 2014 MFMER slide-6

7 Manual Outline by Experts Lesion volume Manual Outline: Precise Accurate Rapid 2014 MFMER slide-7

8 Level Set Methods Lesion volume Level Sets: Precise Accurate Rapid 2014 MFMER slide-8

9 Diameterbased Manual outline Level sets Precise Accurate Rapid 2014 MFMER slide-9

10 Diameterbased Manual outline Level sets Precise Accurate Rapid 2014 MFMER slide-10

11 Long Term Goal Develop an algorithm for Precise, Accurate, and Rapid volume measurement with GPUs recise get close results repeatedly ccurate close to truth apid routinely used in clinical practice Shoot for! 2014 MFMER slide-11

12 MFMER slide-12

13 GPU Level Set Method [Roberts et al. GTC 2010] Speed function = User specifies three parameters: 1) Grayscale level 2) Grayscale window 3) α Roberts, M., Packer, J., Sousa, M. C., & Mitchell, J. R. (2010). A Work-Efficient GPU Algorithm for Level Set Segmentation. High Performance Graphics MFMER slide-13

14 GPU Level Set Method [Roberts et al. GTC 2010] A work- and step-efficient GPU level sets algorithm using CUDA Real-time interactivity enabled by GPU rendering 14x faster than previous GPU algorithm Roberts, M., Packer, J., Sousa, M. C., & Mitchell, J. R. (2010). A Work-Efficient GPU Algorithm for Level Set Segmentation. High Performance Graphics MFMER slide-14

15 Validation: Meningiomas (brain tumor) Diameters Manual GPU Level set 25 patients, 3 experts, 450 measurements Dang, M., Modi, J., Roberts, M., Chan, C., & Mitchell, J. R. (2013). Validation study of a fast, accurate, and precise brain tumor volume measurement. Computer Methods and Programs in Biomedicine, 111(2), MFMER slide-15

16 Diameterbased Manual outline Level sets GPU Level sets Precise Accurate Rapid 2014 MFMER slide-16

17 Proposed improved GPU level sets 1. Utilize multi-contrast images CT, MRI, Dual Energy CT, PET, Co-registration 2014 MFMER slide-17

18 Proposed improved GPU level sets 2. Determine the speed function based on initial samples Principal component analysis (PCA) K nearest neighbor (KNN) classification Manual set PCA KNN Red: foreground sample Blue: background sample 2014 MFMER slide-18

19 Proposed improved GPU level sets 3. Auto optimize curvature parameter (α) based on simulations using GPUs 2014 MFMER slide-19

20 Liver Tumor Volume Dual Energy CT Size: 512x512x96 PCA-based speed function for surface growth 2014 MFMER slide-20

21 Precise Accurate Rapid GPU Level sets New GPU Level sets MFMER slide-21

22 Validation: Intracerebral Hemorrhage (Bleeding Stroke) Ongoing project 78 patients, 4 experts, 1560 measurements 2014 MFMER slide-22

23 MFMER slide-23

24 Other Applications 1. Brain structure volume 2. Vessel segmentation and aneurysm 2014 MFMER slide-24

25 Brain volume explains a lot! 2014 MFMER slide-25

26 Brain volume Brain volume changes are associated with childhood development healthy aging neurological disorders psychiatric disorders Brain segmentation is a fundamental step of morphometric studies MFMER slide-26

27 White and gray matter MRI T1 and T2 Size: 181x217x181 KNN-based speed function for surface growth BrainWeb: Simulated Brain Data MFMER slide-27

28 GPU enables amazing discovery! Accuracy (Dice coefficient) Image noise level (SNR) Curvature parameterα Accuracy (Dice coefficient) The empirical relationship between accuracy, image noise level, and α. It is found by performing white matter segmentations 1000 times MFMER slide-28

29 Vessel segmentation David Frakes, Ph.D; Justin Ryan Image Processing Applications Laboratory MFMER slide-29

30 Aneurysm and treatment simulation Aneurysm David Frakes, Ph.D; Justin Ryan Image Processing Applications Laboratory MFMER slide-30

31 Aneurysm and treatment simulation Sub-voxel difference! Compare to manual outline Distance Difference map (mm) Cut current 4 hours of manual labeling and post-processing into 1 hour. David Frakes, Ph.D; Justin Ryan Image Processing Applications Laboratory MFMER slide-31

32 Sum Up Measuring object volume (3D) in medical images is critical for clinical decision making. Current clinical methods are rapid but inaccurate. Serial computer algorithms are accurate but time consuming. New GPU and CUDA algorithms allow recise, ccurate, and apid methods that could revolutionize clinical management in oncology and other areas. Shoot for! 2014 MFMER slide-32

33 Acknowledgement Christine Zwart and Ross Mitchell Medical Imaging Informatics Lab Mayo Clinic, Scottsdale, Arizona David Frakes and Justin Ryan Image Processing Applications Lab Arizona State University, Tempe, Arizona 2014 MFMER slide-33

34 Questions 2014 MFMER slide-34