CACTUSS: Common Anatomical CT-US Space for US examinations

TL;DR

CACTUSS introduces a novel common anatomical space bridging CT and US imaging, enabling improved automatic ultrasound screening for abdominal aortic aneurysm by leveraging deep learning and intermediary representations.

Contribution

It proposes a new CT-US common space and a segmentation framework that enhances ultrasound AAA diagnosis using knowledge from CT data and deep learning techniques.

Findings

Achieves high Dice scores comparable to supervised methods

Meets clinical diagnostic requirements for AAA screening

Demonstrates effective cross-modality segmentation performance

Abstract

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.