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.

翻译：胸主动脉夹层和动脉瘤是主动脉最致命的疾病。治疗的主要障碍在于医学影像的准确分析。更具体而言，三维图像的主动脉分割通常繁琐且困难。基于深度学习的分割模型是理想的解决方案，但其在复杂病例中无法提供可用输出以及较高的计算成本，导致其临床采用率仍然有限。本研究提出了一种利用目标感兴趣区域检测实现高效主动脉分割的创新方法。与经典检测模型相比，我们提出了一种可广泛应用于单ROI检测的简洁高效检测模型。该检测模型采用多任务训练方式，使用编码器-解码器架构进行分割，并在瓶颈层连接全连接网络进行检测。我们比较了三种方法的性能：应用于完整图像的一步分割模型、nnU-Net模型以及由检测和分割步骤级联构成的模型。实验结果表明，在仅使用三分之一计算资源的情况下，我们的方法取得了0.944的平均Dice相似系数，所有病例均超过0.9。该简洁方案在保持紧凑性和鲁棒性的同时实现了最先进的性能，是临床应用的理想解决方案。