Computer based delineation and follow-up multisite abdominal tumors in longitudinal CT studies

Transcription

1 Research plan submitted for approval as a PhD thesis Submitted by: Refael Vivanti Supervisor: Professor Leo Joskowicz School of Engineering and Computer Science, The Hebrew University of Jerusalem Computer based delineation and follow-up multisite abdominal tumors in longitudinal CT studies

2 Introduction (1) Radiological follow-up of solid tumors is the cornerstone of modern oncology. Cause of abdominal cancer death (2008): Lung cancer: 1.4 million deaths per year Stomach cancer: 740,000 deaths per year Liver cancer: 700,000 deaths per year For any treatment a series of abdominal scans are needed To evaluate the progression of the tumor To evaluate the effectiveness of the treatment

3 Introduction (2) Currently, radiologists perform the initial diagnosis and follow-up using simple guidelines RECIST The product of the largest distance between in-tumor points and the maximum perpendicular diameter Only a rough approximation from a single 2D projection image For 3D CT, the tumor volume is estimated with ellipsoid formula: max-length max-depth max-width Research shows that 3D volumetric measurements provide the best information for tumor progress monitoring.

4 Linear measurements [Schwartz and Zhao. 2010] Pre Therapy Long axis: 25.0mm Short axis: 20.4mm Volume: 3,420 mm 3 After 24 days Long axis: 25.9mm Short axis: 19.8mm Volume: 4,608 mm 3 Change in Long axis 4% Change in Volume 35%

5 Volumetric measurements: Study: [Weltens 2001] Axial MRI slice Nine independent observers Repeated delineations Inter/intra observer variability: 30% Key issue: fuzzy tumor boundaries

6 Introduction (3) Measuring volume manually / semi-automaticly. Time-consuming: done on each slice separately Requires expert knowledge User dependent: intra- and inter- observer variability. Current methods focus on a single organ The patient might have tumors in multiple sites A multisite segmentation method is desirable Can give a better understanding of the disease course

7 Introduction (4) The abdominal sites: the liver and the lungs. Liver difficulties Ambiguity of the liver and tumors boundaries Complexity of the tumors surfaces Contrast variability between the parenchyma, vessels, and tumors Different tumor sizes and shapes Possible presence of many small metastases. Lungs difficulties Similarity to normal anatomies such as the lung nodules, Small size of the tumors.

8 Previous work (1) Medical image seg. is widely researched. However, abdominal tumor segmentation in follow-up study framework is a new territory. Relevant research: Liver tumor segmentation. Lung-tumors segmentation. Follow-up studies in medical image processing.

9 Previous work (2) Liver tumor segmentation Segmentation by threshold, followed by deformable model refinement. Markov Random Field estimation coupled with deformable models. Interactive region-growing. Classify 1D intensity profiles of the tumors. Solving an energy function describing the propagation of the classified voxels MICCAI2008 challenge: Liver tumor segmentation

10 Previous work (3) Lung-tumors segmentation [AUTHOR1, 2009] All on registered PET/CT scans Thresholding and connected-components analysis Unsupervised Maximum A Posterior MRF extended to a vectorial approach. Classify tumors to 5 shape-categories, segmentation based on a mixed internal/external force and on a cluster function.

11 Previous work (4) Follow-up studies in medical image processing Brain tumors rigid registration: Registration and change detection Find which boundaries were changed, and redefine them. [Weizman et al ] Lung tumors non-rigid registration: Jiajing et al - adaptive region growing and clustering Opfer et al - model-based segmentation Both on the less common PET/CT scans

12 Goals A generic methodology for nearly automatic tumor mass delineation on a baseline abdominal CT scan A clinician-oriented, fully automatic analysis method for patient-specific multi-mass and multiorgan, longitudinal lung and liver studies To conduct retrospective preliminary comparative clinical validation studies based on clinical scans and radiologist ground truth tumor delineations.

13 Method overview

14 Method components (1) Baseline scan analysis Semi-automatic: user initial markings and corrections.

15 Method components (2) Region Of Interest Identification To reduce running time and increase accuracy Lungs: using threshold for low intensity. Liver: between the lungs and the kidney.

16 Method components (3) Registration of new scan and baseline Deformable registration model. Mutual information registration grade. Multi-stage registration scheme: Global and then local Pure translation, affine registration and then deformable registration

17 Method components (4) Segmentation of tumors in new scan The best model is the patient itself The transformed segmentation is a good prior: Statistical approach Morphological approach

18 Method components (5) Detection of new tumors in new scan Statistical information from old tumors in the same scan A prior on the size and shape of young tumors

19 Method components (6) Validation study Database of follow-up studies Ground Truth - delineations made by a radiologist analysis of the tumor progression over time

20 Preliminary results (1) Prototypes of some methods are implemented Initial delineation Automatic follow-up study We tested them on datasets: Liver follow-up dataset: 10 cases Lungs follow-up dataset: currently 1 case

21 Preliminary results (2) Initial delineation semi-automatic Inputs: Seg. on central slice Thresholds and ROI Algorithms: Moving segmentation from slice to slice Connected components analysis. Graphic User Interface: Input markers Post-process tools

22 Preliminary results (3) Automatic follow-up study Prototype of main stages: Registration Segmentation Thresholds Connected components Leaks correction Tested on database Proof of concept Work needed for robustness and accuracy

23 Base scan of lung tumor from 14/10/2010

24 Base scan with our semi-automatic delineation

25 Follow-up scan from 3/1/2011 (2.5 moths later)

26 Follow-up scan with our automatic delineation

27 Liver tumor 5 May May 2011

28 Before registration

29 Before registration

30 Before registration

31 Global Affine Registration

32 Local B-spline Registration

33 Segmentation results

34 Follow-up comparison

35 Follow-up comparison

36 Research Plan 1. A semi-automatic method for initial delineation of abdominal tumors. 2. An automatic Region Of Interest (ROI) identification. 3. Registration 4. Segmentation 5. New lesions 6. Implementation 7. Validation