Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation
This work addresses the need for accurate segmentation in diagnosing coronary artery diseases, but it is incremental as it benchmarks existing methods on a new dataset.
The paper tackled the problem of coronary artery segmentation by evaluating 25 encoder-decoder architectures on the ASOCA dataset, finding that the EfficientNet-LinkNet combination achieved a Dice coefficient of 0.882 and a 95th percentile Hausdorff distance of 4.753.
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.