## 3 Experimental Results
### Method Evaluation  
The outcomes, obtained by the automatic proposed method, are presented in Figures 7-9. In addition, the algorithm results (in green) and the ground truths (in red) are overlaid on a selected slice of the 3D iB-mode images for visualization purposes. Table 2 summarizes the qualitative and quantitative evaluation. The former is based on expert observations, and the latter is performed by using the overlap, accuracy, area under the curve and error rate measures. The experiments showed that our approach succeeded in detecting the position of all tumor remnant areas in 15 out of 19 patients (\(Overlap\geq 0.5\)). For these cases, a qualitative coding of \(1/-1\) (all tumorous regions were detected) or \(1/1\) (all tumorous regions were detected and extra suspected regions, as well) was observed. Regarding the four unsuccessful cases, the areas with tumorous tissue were partially detected in two patients (Patients 2 and 7, where \(Overlap<0.5\)), and the algorithm failed in the two other cases (Patients 14 and 18, where \(Overlap=0\)). One failure reason is the position of tumor residuals near the image top (Patients 7 and 18). These areas are removed in the preprocessing steps to eliminate artifacts caused by the US probe. The method was also tested on patient data from the set B where false positives were detected in the cases of Patients 20 and 23 and none for Patients 21 and 22. Figure 7: Results of residual tumor identification from Patients 1 to 6. The results obtained with the proposed automatic method (in green) and in the manual segmentation (in red) are overlaid on a selected slice of the 3D iB-mode image data. The algorithm missed tumorous structures in Patient 2 and identified extra regions in Patient 4. Figure 8: Results of residual tumor identification from Patients 7 to 12. The results obtained by using the proposed automatic method (in green) are superimposed with the expert manual segmentation (in red). The algorithm missed the detection of other tumorous structures in the case of Patient 7, and it identified a large region in the case of Patient 10. Figure 9: Results of residual tumor identification from Patients 13 to 19. The results obtained by using the proposed automatic method (in green) are overlaid with the expert manual segmentation (in red). The algorithm missed completely the target in the case of Patients 14 and 18. In addition, it detected an extra region in the case of Patient 15. Additionally, three cases that include false positives were found (Patients 4, 14, 18). These areas correspond to hyperechogenic structures (for example, bone and blood on the cavity border) in both iB-mode and iCEUS image data, and they are therefore extracted by the method. However, when the false positives are detected in areas far away from the resection cavity (e.g., Patients 4 and 18), these outcomes do not affect the clinical interpretation of the data because tumor residuals can be found only in the cavity. In general, the quantitative metric used for estimating the tumor residuals' localization sustains the expert classifications. \(Overlap\) values lower than 0.5 were obtained when areas with tumor residuals were partly or not detected by the approach (Patients 2, 7, 14 and 18). However, the absolute value of the \(Overlap\) coefficients does not provide a quality rate about the segmentation of tumor remnants. For instance, a value of 1 was reached for Patient 10 because the boxes were included in each other, but this case does not show the best visual result. The other metrics measure objectively the voxel classification quality. The highest accuracy values (\(Acc\geq 0.97\)) and lowest error rate (\(Err<0.03\)) were obtained for Patients 1, 3, 6, 16, 17 and 19, because the algorithm detected correctly most of the true positives. Moreover, good accuracy scores (\(0.93\leq Acc\leq 0.96\)) and error rates (\(Err<0.08\)) were reached in the cases of Patients 4, 8, 9, 11, 12, 13 and 15.