CACTUSS: Common Anatomical CT-US Space for US examinations
This work addresses the challenge of operator-dependent and low-quality US imaging for AAA diagnosis, offering an incremental improvement by transferring knowledge from CT to enhance automated segmentation.
The authors tackled the problem of automating abdominal aortic aneurysm (AAA) screening in ultrasound (US) by leveraging CT data, proposing CACTUSS as a common anatomical space to bridge modalities, resulting in a method that meets clinical requirements with quantitative improvements in Dice Score and diagnostic metrics.
Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and treatment planning. Recently, abdominal CT datasets have been successfully utilized to train deep neural networks for automatic aorta segmentation. Knowledge gathered from this solved task could therefore be leveraged to improve US segmentation for AAA diagnosis and monitoring. To this end, we propose CACTUSS: a common anatomical CT-US space, which acts as a virtual bridge between CT and US modalities to enable automatic AAA screening sonography. CACTUSS makes use of publicly available labelled data to learn to segment based on an intermediary representation that inherits properties from both US and CT. We train a segmentation network in this new representation and employ an additional image-to-image translation network which enables our model to perform on real B-mode images. Quantitative comparisons against fully supervised methods demonstrate the capabilities of CACTUSS in terms of Dice Score and diagnostic metrics, showing that our method also meets the clinical requirements for AAA scanning and diagnosis.