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The proposed AI pipeline for diagnosing BDTT. First, CT images in the portal venous phase with tumors were selected, then the selected images were center-cropped and resized to a unified size. After filtering and preprocessing, the trained DBD detector was applied to identify DBDs on the resulting images. Finally, a patient was diagnosed as HCC with BDTT if the image-level positive proportion (I-PP) was greater than the optimal threshold.
Figures of the Article
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(a) A CT image of an HCC patient with BDTT. (b) A CT image with four labeled bounding boxes for DBDs. (c1)–(c3) Three consecutive CT images of an HCC patient with BDTT. In all images, tumors were marked in ellipses and DBDs caused by BDTT were marked in capsules.
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The proposed AI pipeline for diagnosing BDTT. First, CT images in the portal venous phase with tumors were selected, then the selected images were center-cropped and resized to a unified size. After filtering and preprocessing, the trained DBD detector was applied to identify DBDs on the resulting images. Finally, a patient was diagnosed as HCC with BDTT if the image-level positive proportion (I-PP) was greater than the optimal threshold.
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CT images of an HCC patient with BDTT at the same location from (a) plain scan phase, (b) arterial phase, (c) portal venous phase, and (d) delayed phase. DBDs caused by BDTT were marked in capsules.
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Flow chart of Faster R-CNN. The pre-processed CT images were first transformed via DAM, then CNN backbone extracted multiscale feature maps from the transformed image. Based on the extracted features, at the first stage, RPN output proposal regions which tend to contain DBDs, then at the second stage, R-CNN further classified the proposal regions and refined their coordinates as the final outputs.
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Several examples of successfully detected DBDs: (a)–(d) were the ground truth annotations of CT images of four different HCC patients with BDTT (tumors were marked in ellipses and DBDs caused by BDTT were marked in rectangles); (a')–(d') were the corresponding bounding boxes output by Faster R-CNN.
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Box plots of image-level positive proportions (I-PPs) for the case group and the matched control group. I-PPs were calculated from the output results of the three different detectors, respectively, and the P-values for difference comparison were calculated by the Wilcoxon Mann-Whitney test.
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ROC curves of the proposed AI pipeline with Faster R-CNN (solid line), Reppoints (longdash line), Foveabox (dotdash line), and random forest (dotted line). The corresponding AUCs were 0.94, 0.92, 0.89, and 0.71, respectively. The corresponding sensitivities corresponding to the optimal threshold values were 0.81, 0.75, 0.88, and 0.69, respectively. The corresponding specificities corresponding to the optimal threshold values were 1.00, 0.94, 0.88, and 0.75, respectively.
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The first row shows two samples of undetected DBDs: (al) tiny DBD and (a2) inconspicuous DBD. The second row shows three types of dominant false positive bounding boxes: (b1) DBD-like liver tumor region and (b2) gap between liver and other tissues, (b3) irrelevant structures outside the liver region
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