Computer-aided diagnosis in lung CT image analysis

1. Computer-aided diagnosis in lung CT image analysis The state of the art in pulmonary CAD design and implementation. Matteo Santoro

2. Overview • Over the last decade or so, many investigators have carried out basic studies and clinical applications toward the development of modern computerized schemes for detection and characterization of lesions in radiological images, based on computer vision and artificial intelligence. • CAD may be defined as a diagnosis made by a physician who takes into account the results of the computer output as a ‘second opinion’. In radiology, the computer output is derived from quantitative analysis of diagnostic images. • It is important to note that the computer is used only as a tool to provide additional information to clinicians, who will make the final decision as to the diagnosis of a patient. Therefore, the basic concept of CAD is clearly different from that of ‘automated diagnosis’. • The purpose of CAD in radiology is to improve the diagnostic accuracy as well as the consistency of radiologists’ image interpretation by using the computer output as a guide. The computer output can be very helpful because a radiologist’s diagnosis is made based on subjective judgment: radiologists tend to miss lesions such as lung nodules and, in addition, variations in diagnosis, such as inter-observer and intra-observer variations, can be very large.

3. Summary Introduction to the problem of nodules detection in lung CT images. Most accepted hypotheses underlying the algorithm design. General guidelines of proposed CAD systems. Results description and evaluation. Conclusive remarks.

4. Nodules detection in CT images • Sensitivity of CT for the detection of lung nodules is superior to that of chest radiography (30%–40% of potentially detectable lung cancers are missed). • CT enables to distinctly represent anatomic structures that would otherwise project in superposition in a chest radiograph. • However, identification of small lung nodules is confounded by the prominence of blood vessels in CT images. • Distinguishing between nodules and vessels typically requires visual comparison among multiple CT sections, each of which contains information that must be evaluated by a radiologist and assimilated into the larger context of the volumetric data acquired during the scan.

5. Nodules detection in CT images

6. Nodules detection in CT images

7. Nodules detection in CT images

8. Nodules detection in CT images

9. And the 3rd dimension? • 2D+1 vs. 3D analysis • 3D: the higher the number of the slice the better the z-axis resolution; • 2D+1: single image-based approach + reasoning system • pseudo-time analysis?

11. A model for nodules • Nodules within the lungs are spherical objects • GAUSSIAN (2D): • SPHERICAL (3D): • Nodules adjacent to the pleura need specific treatment!!

12. Detection of juxta-pleural nodule candidates • Armato et al.: rolling ball • Ko et al.: comparing the curvature at points on the lung border. • Gurcan et al.: indentation detection

13. Closed world assumptions • Gray-levels are strongly related to specific tissue being observed. (CT number or Hunsfield units). • The CT values of the mediastinum and lung walls are much higher than that of the lung tissue. • Threshold-based approaches, or similar, can be useful. • X,Y resolution is quite fixed. • Anatomical chest structures are well know.

14. Data set construction • In the 40% of cases high dose CT scans are used. • The total number of collected patients is very often less than 50. • The total amount of normal cases is very low, regardless of it will be more than the 90% in the screening analysis (I hope!). • A number of cases are excluded because of the presence of four or more pulmonary nodules(?), a pulmonary nodule larger than 3 cm in diameter, severe pulmonary fibrosis, diffuse bronchiectasis, or extensive inflammatory scars.

15. Gurcan et al. 2002 • Three regions for the entire lung volume • CT lung density varies according to the depth of inspiration, beam collimation, and calibration of the CT scanner. Therefore, they implemented and adaptive scheme instead of using a constant (or a range of) threshold value to segment the lung regions. • k-means algorithms

16. Gurcan et al. 2002 Feature extraction volume, surface area, average gray value, standard deviation, skewness and kurtosis of the gray value histogram. Classification Linear discriminant analysis classifier

17. Armato et al. 2001 • Multiple thresholds are used to segment the lung section-by-section.

18. Armato et al. 2001 • The segmented lung volume is subjected to a multiple gray-level thresholding procedure. • Thirty-six gray-level thresholds ranging from a gray level of 50 to a gray level of 225 (in increments of 5) are successively applied to the 10- • bit segmented lung volume. • At a given threshold, all pixels with associated gray levels less than the threshold are suppressed within the segmented lung volume. • A region-labeling technique that employs 18 connectivity is applied to remaining pixels to group contiguous structures in three dimensions. • The geometric volume (expressed in mm3) of each identified structure is calculated as the product of the number of pixels in the structure, the square of the pixel dimension, and the section thickness • A nodule is defined radiologically as any well-demarcated, soft-tissue focal opacity with diameter less than 3 cm.

19. Armato et al. 2001

20. Armato et al. 2001 • mean gray level of the candidate, • gray-level standard deviation, • gray-level threshold at which the candidate was identified, • volume, • sphericity, • radius of the sphere of equivalent volume. • eccentricity, • circularity, • compactness.

21. Ko et al. 2001 • Thorax and lung border detection (binary image). • Backtracking algorithm for border tracing. • Determination of candidate regions. • Computation of properties of candidate regions. • Analysis of consecutive CT sections. • Analysis of change over time… (?).

22. Ko et al. 2001

23. Kostis et al. 2003 • Well-circumscribed: The nodule is located centrally in the lung, without significant connections to vasculature. • Vascularized: The nodule is located centrally in the lung, but has significant vascularization (connections to neighboring vessels). • Pleural tail: The nodule is near the pleural surface, connected by a thin structure (“pleural tail”). • Juxtapleural: A significant proportion of the nodule periphery is connected to the pleural surface. • Nearly half of the nodules are vascularized, approximately one-third are well circumscribed, nearly one-quarter are juxtapleural, and a small minority (approximately 1%) have pleural tails. • Techniques for the segmentation of each of these nodule classes differ as the varied local geometry is not amenable to a single method. • It is necessary, therefore, to formulate mathematical models of each class and develop separate segmentation schemes accordingly.

24. Kostis et al. 2003

25. Kostis et al. 2003

26. Kostis et al. 2003 • Image acquisition and pre-processing • Anisotropic Data • Segmentation • Thresholding • Connected Component Analysis • Morphological Processing • Volumetric Doubling Time Estimation

27. Sluimer et al. 2003

28. Sluimer et al. 2003 • Mean • Standard deviation • Skew • Kurtosis • Linear discriminant classifier • Quadratic discriminant classifier • Support vector machine • K-nearest-neighbors classifier

29. McNitt-Gray et al. 1999

30. Lee et al. 2001

31. Algorithm details • Non-specific descriptions of used algorithms are widely accepted. • Papers as Acta Radiologica, Radiology, are useful for “cut and paste” to write introductions. • Medical Physics and IEEE Trans. On Medical Imaging must be preferred.

32. Time analysis • Almost all papers do not reveal the time necessary to analyze single images or the entire series.

33. Conclusive remarks • New approaches vs. safe approaches • A standardization of partial results evaluation is a very critical issue. • It seems that implementation tricks and ad-hoc parameter values selection play a central role for the success of CAD

34. Bibliography (1) • S.J.Swensen et al.,2002, Screening for Lung Cancer with Low-Dose Spiral Computed Tomography, Am J Respir Crit Care Med, 165,508–513. • K.Away et al., 2004, Pulmonary Nodules at Chest CT: Effect of Computer-aided Diagnosis on Radiologists’ Detection Performance, Radiology, 230, 347-352. • M.N.Gurcan et al.,2002, Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system, Med. Phys. 29, 2552-2558. • Armato et al. 2001, Automated detection of lung nodules in CT scans: Preliminary results, Med. Phys. 28, 1552-1561. • Ko et al., 2001, Chest CT: Automated Nodule Detection and Assessment of Change over – Preliminary Experience, Radiology, 218, 267-273. • Kostis et al., 2003, Three-Dimensional Segmentation and Growth-Rate Estimation of Small Pulmonary Nodules in Helical CT Images, IEEE Trans. On Medical Imaging, 22, 1259-1274. • Sluimer et al., 2003, Computer-aided diagnosis in high resolution CT of the lungs, Med. Phys., 30, 3081-3090.

35. Bibliography (2) • McNitt-Gray et al., 1999, A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: Preliminary results, Med. Phys., 26, 880-888. • Lee et al., 2001, Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template-Matching TechniqueI, IEEE Trans. On Medical Imaging, 20, 595-604. • El-Bazl et al., 2003, Automatic identification of lung abnormalities in chest spiral CT scans, in Proc. of ICASSP03.