Anatomically constrained CT image translation for heterogeneous blood vessel segmentation
This work addresses the challenge of blood vessel segmentation in medical imaging by reducing radiation dose for patients, though it is incremental as it extends an existing CycleGAN approach.
The paper tackled the problem of synthesizing contrast-enhanced CT images from non-contrast CT to avoid double radiation exposure, achieving qualitative and quantitative improvements over state-of-the-art methods in image translation tasks.
Anatomical structures such as blood vessels in contrast-enhanced CT (ceCT) images can be challenging to segment due to the variability in contrast medium diffusion. The combined use of ceCT and contrast-free (CT) CT images can improve the segmentation performances, but at the cost of a double radiation exposure. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. The CycleGAN approach has recently attracted particular attention because it alleviates the need for paired data that are difficult to obtain. Despite the great performances demonstrated in the literature, limitations still remain when dealing with 3D volumes generated slice by slice from unpaired datasets with different fields of view. We present an extension of CycleGAN to generate high fidelity images, with good structural consistency, in this context. We leverage anatomical constraints and automatic region of interest selection by adapting the Self-Supervised Body Regressor. These constraints enforce anatomical consistency and allow feeding anatomically-paired input images to the algorithm. Results show qualitative and quantitative improvements, compared to stateof-the-art methods, on the translation task between ceCT and CT images (and vice versa).