Figure 2. Coarse lesion detection results of (a) accurately classified and (b) misclassified examples in target domains, LC-A and LC-B, relative to the source, P-x. All-sequence-ensembled (T2, ADC, hDWI) approach is employed. In lesion detection results (1st and 2nd rows), The lesions (ground-truth) are pointed in green. The predicted coarse lesion regions are colored in yellow. Promising prediction of lesion region, i.e., containing the ground-truth in all sequences, can yield the higher correctness of classification as in (a). Moreover, under-segmented prostate regions marked with yellow boxes/contours (i.e., the example of LC-B in the 3rd row) would also worse the classification outcome.
Figure 2 shows coarse lesion detection results of the accurately classified and misclassified examples. Two DA settings (i.e. P-x to LC-A, and P-x to LC-B) were selected as representatives for lesion detection evaluation. Results of the all-sequence-ensembled method are selected as representative for analysis. In the correctly classified examples, Coarse lesion contours could encircle the lesion ground-truth point in all sequences (as shown in Figure 2a). However, in the unclassified examples, the coarse lesion position could not be precisely detected in most sequences as shown in the third row. In the example of LC-A, the lesion on T2 is correctly detected, but the lesion contours on ADC and hDWI maps are falsely identified. The possible reason is that the coarse lesion masks applied as the training ground truth could not depict the actual lesion contours accurately. Therefore, we can observe that accurate detection on ADC and hDWI also play a role in enhancing the ensembled classification, although lesion detection generally heavily relies on T2 images. In the future, robust weak label processing methods (e.g., deep extreme level set evolution method [15]) are expected to be employed. For the example from LC-B, under-segmentation of the prostate region can be found on the T2 image, which could lead to failure lesion detection. As the prostate regions on ADC and hDWI were transformed using T2, under/over-segmentation of the prostate gland on T2 would deteriorate the lesion detection in the other two sequences. Despite the inaccurate lesion detection on ADC and hDWI, it should be noted that the models with multi-sequences input still outperform the models using T2 alone in lesion classification, accrediting to the re-use of prostate features from ADC and hDWI.