Deep Supervision by Gaussian Pseudo-label-based Morphological Attention for Abdominal Aorta Segmentation in Non-Contrast CTs
This work addresses segmentation challenges for computer-assisted endovascular navigation in medical imaging, but it is incremental as it builds on existing deep learning models with a novel pseudo-label approach.
The paper tackled the problem of segmenting the abdominal aorta in non-contrast CT images, where ambiguous boundaries can lead to overfitting, by introducing a Gaussian-based pseudo-label with deep supervision to enhance morphological attention, resulting in improved performance validated on a dataset of 30 CT volumes with 5749 slices.
The segmentation of the abdominal aorta in non-contrast CT images is a non-trivial task for computer-assisted endovascular navigation, particularly in scenarios where contrast agents are unsuitable. While state-of-the-art deep learning segmentation models have been proposed recently for this task, they are trained on manually annotated strong labels. However, the inherent ambiguity in the boundary of the aorta in non-contrast CT may undermine the reliability of strong labels, leading to potential overfitting risks. This paper introduces a Gaussian-based pseudo label, integrated into conventional deep learning models through deep supervision, to achieve Morphological Attention (MA) enhancement. As the Gaussian pseudo label retains the morphological features of the aorta without explicitly representing its boundary distribution, we suggest that it preserves aortic morphology during training while mitigating the negative impact of ambiguous boundaries, reducing the risk of overfitting. It is introduced in various 2D/3D deep learning models and validated on our local data set of 30 non-contrast CT volumes comprising 5749 CT slices. The results underscore the effectiveness of MA in preserving the morphological characteristics of the aorta and addressing overfitting concerns, thereby enhancing the performance of the models.