  Using radiological scans to identify liver tumors is crucial for proper
patient treatment. This is highly challenging, as top radiologists only achieve
F1 scores of roughly 80% (hepatocellular carcinoma (HCC) vs. others) with only
moderate inter-rater agreement, even when using multi-phase magnetic resonance
(MR) imagery. Thus, there is great impetus for computer-aided diagnosis (CAD)
solutions. A critical challenge is to robustly parse a 3D MR volume to localize
diagnosable regions of interest (ROI), especially for edge cases. In this
paper, we break down this problem using a key-slice parser (KSP), which
emulates physician workflows by first identifying key slices and then
localizing their corresponding key ROIs. To achieve robustness, the KSP also
uses curve-parsing and detection confidence re-weighting. We evaluate our
approach on the largest multi-phase MR liver lesion test dataset to date (430
biopsy-confirmed patients). Experiments demonstrate that our KSP can localize
diagnosable ROIs with high reliability: 87% patients have an average 3D overlap
of >= 40% with the ground truth compared to only 79% using the best tested
detector. When coupled with a classifier, we achieve an HCC vs. others F1 score
of 0.801, providing a fully-automated CAD performance comparable to top human
physicians.
