Region of interest detection for efficient aortic segmentation

Thoracic aortic dissection and aneurysms are the most lethal diseases of the aorta. The major hindrance to
treatment lies in the accurate analysis of the medical images. More particularly, aortic segmentation of the 3D
image is often tedious and difficult. Deep-learning-based segmentation models are an ideal solution, but their
inability to deliver usable outputs in difficult cases and their computational cost cause their clinical adoption
to stay limited. This study presents an innovative approach for efficient aortic segmentation using targeted
region of interest (ROI) detection. In contrast to classical detection models, we propose a simple and efficient
detection model that can be widely applied to detect a single ROI. Our detection model is trained as a multi-task
model, using an encoder-decoder architecture for segmentation and a fully connected network attached to the
bottleneck for detection. We compare the performance of a one-step segmentation model applied to a complete
image, nnU-Net and our cascade model composed of a detection and a segmentation step. We achieve a mean
Dice similarity coefficient of 0.944 with over 0.9 for all cases using a third of the computing power. This simple
solution achieves state-of-the-art performance while being compact and robust, making it an ideal solution for
clinical applications.