Abstract
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 key-slice prediction (KSP), which emulates physician workflows by predicting the slice a physician would choose as “key” and then localizing the 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 \({\ge }40\%\) 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.
B. Lai, Y. Wu and X. Bai—Equal contribution.
This work was done when X. Bai was an intern at Ping An Technology.
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Lai, B. et al. (2021). Liver Tumor Localization and Characterization from Multi-phase MR Volumes Using Key-Slice Prediction: A Physician-Inspired Approach. In: Rekik, I., Adeli, E., Park, S.H., Schnabel, J. (eds) Predictive Intelligence in Medicine. PRIME 2021. Lecture Notes in Computer Science(), vol 12928. Springer, Cham. https://doi.org/10.1007/978-3-030-87602-9_5
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