  Multi-phase computed tomography (CT) scans use contrast agents to highlight
different anatomical structures within the body to improve the probability of
identifying and detecting anatomical structures of interest and abnormalities
such as liver lesions. Yet, detecting these lesions remains a challenging task
as these lesions vary significantly in their size, shape, texture, and contrast
with respect to surrounding tissue. Therefore, radiologists need to have an
extensive experience to be able to identify and detect these lesions.
Segmentation-based neural networks can assist radiologists with this task.
Current state-of-the-art lesion segmentation networks use the encoder-decoder
design paradigm based on the UNet architecture where the multi-phase CT scan
volume is fed to the network as a multi-channel input. Although this approach
utilizes information from all the phases and outperform single-phase
segmentation networks, we demonstrate that their performance is not optimal and
can be further improved by incorporating the learning from models trained on
each single-phase individually. Our approach comprises three stages. The first
stage identifies the regions within the liver where there might be lesions at
three different scales (4, 8, and 16 mm). The second stage includes the main
segmentation model trained using all the phases as well as a segmentation model
trained on each of the phases individually. The third stage uses the
multi-phase CT volumes together with the predictions from each of the
segmentation models to generate the final segmentation map. Overall, our
approach improves relative liver lesion segmentation performance by 1.6% while
reducing performance variability across subjects by 8% when compared to the
current state-of-the-art models.
