Assessing Encoder-Decoder Architectures for Robust Coronary Artery
  Segmentation
Shisheng Zhang Ramtin Gharleghi Sonit Singh Arcot Sowmya Susann Beier
Coronary artery diseases are among the leading causes of mortality worldwide.
Timely and accurate diagnosis, facilitated by precise coronary artery
segmentation, is pivotal in changing patient outcomes. In the realm of
biomedical imaging, convolutional neural networks, especially the U-Net
architecture, have revolutionised segmentation processes. However, one of the
primary challenges remains the lack of benchmarking datasets specific to
coronary arteries. However through the use of the recently published public
dataset ASOCA, the potential of deep learning for accurate coronary
segmentation can be improved. This paper delves deep into examining the
performance of 25 distinct encoder-decoder combinations. Through analysis of
the 40 cases provided to ASOCA participants, it is revealed that the
EfficientNet-LinkNet combination, serving as encoder and decoder, stands out.
It achieves a Dice coefficient of 0.882 and a 95th percentile Hausdorff
distance of 4.753. These findings not only underscore the superiority of our
model in comparison to those presented at the MICCAI 2020 challenge but also
set the stage for future advancements in coronary artery segmentation, opening
doors to enhanced diagnostic and treatment strategies.