The Fast-Selection Method

All the tumors in slices of the movie file have to be segmented to calculate the hard ratios in the first experiment. However, the segmentation task wastes a lot of time even if the segmentation method used in the proposed system is quite simple.

Indeed, the tumor contour is not really necessary for the computation of the SNRe and the CNRe if the compression displacement is not large. A fast-selection method uses some user-selected points, one for SNRe and three for CNRe, to define the tumor and background regions, as shown in Fig. 24. The central user-selected point is used to define the center of 10×10 tumor box and the upper-left and lower-right user-selected points are used to define the bounding box for the tumor in Fig. 17 for quantifying the

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elastographic quality. After selecting the best quantified quality slices with maximum SNRe and CNRe, their hard ratios are computed. These ratios are represented by Rfsnr

and Rfcnr, respectively. The population pyramids of Rfsnr and Rfcnr are shown in Fig.

25.

To examine the effectiveness of the Rfsnr and Rfcnr, the same statistical evaluations to the first experiment are performed. The median value and the p-value of the Mann-Whitney U test are listed in Table 3. Both the Rfsnr and Rfcnr are statistical significant. The performance indexes are also used to evaluate the Rfsnr and Rfcnr and the result is listed in Table 4. The ROC curves are shown in Fig. 26 and the Az value of the Rfsnr is 0.8287 and the Az value of Rfcnr is 0.8451. Then, to find the relationship between the general quantification and the fast-selection method, the p-value of the ROC curves and the performance indexes are calculated. As illustrated in Table 5 and Table 6, the result of Rfcnr is more similar to the original ratios Rsnr and Rcnr than Rfsnr

in this experiment.

The performance of the ratios used the fast-selection method are worse than the origin Rsnr and Rcnr. The reason is that the fast-selection method uses just the user-selected points to derive the tumor region. However, the positions of the tumor are changing in different slices in a movie file, using the same tumor region will lead the decrease of the accuracy. Another point should be noticed is the Rfcnr is better than Rfsnr in both ROC curve and the performance indexes. The reason is that the fast-selection using SNRe refers to the region generated by the middle user-selected point only. This could make the Rfsnr be easier to be affected by the improper compression. Oppositely, the fast-selection using CNRe refers to not only the tumor region, but also the background region. The background region is much boarder than the tumor region so the effect of the compression could be reduced. Hence, the

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fast-selection method using the CNRe is more appropriate to replace the general quantification methods than the fast-selection using SNRe. Note that the performance of the Rfcnr is very close to the performance of the original quantification methods with tumor segmentation of each slice and its run time is 58 second per case, which is the one-seventh of the run time of the original quantification method.

Fig. 24 The user-selected points ● of the fast-selection method. The central point is used in SNRe and all the three points are used to define the tumor and background regions in CNRe.

(a)

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(b)

Fig. 25 The population pyramid of the (a) Rfsnr and (b) Rfcnr.

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Table 3 The median value and p-value (using Mann-Whitney U test) of Rfsnr and Rfcnr.

Ratio Type Median Value p-value

Rfsnr

Benign 0.0531

<0.001 Malignant 0.5745

Rfcnr

Benign 0.0701

<0.001 Malignant 0.5948

Table 4 The performance indexes of Rfsnr and Rfcnr. Rfsnr Rfcnr

Threshold 0.2983 0.3518 TP 38 38 FN 10 10 TN 71 78 FP 22 15

Accuracy 77.30% 82.27%

Sensitivity 79.17% 79.17%

Specificity 76.34% 83.87%

PPV 63.33% 71.70%

NPV 87.65% 88.64%

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Fig. 26 The ROC curves of the ratios using fast-selection method.

Table 5 The p-values of the ROC curves between the original quantification methods and the fast-selection methods.

Hard Ratios p-value

Rsnr Rfsnr 0.0042

Rsnr Rfcnr 0.0255

Rcnr Rfsnr 0.0850

Rcnr Rfcnr 0.2028

Tab

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(c) (d)

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(i)

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(j)

Fig. 27 A true negative example. (a)(b) The elastographic image and the B-mode image with the segmentation result of the same slice. (c) The slice selected by maximum hard ratio. The hard ratio of this slice is 12.24%. (d) The slice selected by maximum SNRe, 28.77. The hard ratio of this slice is 3.36%. (e) The slice selected by maximum CNRe, 13.18. The hard ratio of this slice is 3.44%. (f) The slice selected by the maximum compression and the hard ratio of the slice is 3.44%. (g) The physician-selected image with the hard ratio 3.29%. (h) The slice selected by the fast-selection using SNRe. The hard ratio is 0.58%. (i) The slice selected by the fast-selection using CNRe. The hard ratio is 12.85%. (j) The curves of different quantification methods. Each value of the quantification is normalized with the maximum value.

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(g) (h)

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Fig. 28 The false positive example. (a)(b) The elastographic image and the B-mode image with the segmentation result of the same slice. (c) The slice selected by maximum hard ratio. The hard ratio of this slice is 84.59%. (d) The slice selected by maximum SNRe,70.08. The hard ratio of this slice is 69.32%. (e) The slice selected by maximum CNRe,12.57. The hard ratio of this slice is 61.31%. (f) The slice selected by the maximum compression and the hard ratio of the slice is 34.08 %. (g) The physician-selected image with the hard ratio 65.83%. (h) The slice selected by the fast-selection using SNRe. The hard ratio is 63.66%. (i) The slice selected by the fast-selection using CNRe. The hard ratio is 70.61%. (j) The curves of different quantification methods.

Each value of the quantification is normalized with the maximum value.

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(b)

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(g) (h)

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Fig. 29 A true positive example. (a)(b) The elastographic image and the B-mode image with the segmentation result of the same slice. (c) The slice selected by maximum hard ratio. The hard ratio of this slice is 82.45%. (d) The slice selected by maximum SNRe,102.59. The hard ratio of this slice is 82.06%. (e) The slice selected by maximum CNRe, 10.60. The hard ratio of this slice is 66.3%. (f) The slice selected by the maximum compression and the hard ratio of the slice is 68.25%. (g) The physician-selected image with the hard ratio 69.75%. (h) The slice selected by the fast-selection using SNRe. The hard ratio is 72.12%. (i) The slice selected by the fast-selection using CNRe. The hard ratio is 63.93%. (j) The curves of different quantification methods. Each value of the quantification is normalized with the maximum value.

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(i)

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Fig. 30 A false negative example. (a)(b) The elastographic image and the B-mode image with the segmentation result of the same slice. (c) The slice selected by maximum hard ratio. The hard ratio of this slice is 19.29%. (d) The slice selected by maximum SNRe,120.78. The hard ratio of this slice is 13.69%. (e) The slice selected by maximum CNRe, 14.88. The hard ratio of this slice is 14.3%. (f) The slice selected by the maximum compression and the hard ratio of the slice is 14.70%. (g) The physician-selected image with the hard ratio 0%. (h) The slice selected by the fast-selection using SNRe. The hard ratio is 5.55%. (i) The slice selected by the fast-selection using CNRe. The hard ratio is 11.49%. (j) The curves of different quantification methods. Each value of the quantification is normalized with the maximum value.

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Chapter 5