Anatomically constrained CT image translation for heterogeneous blood vessel segmentation - Archive ouverte HAL Access content directly
Conference Papers Year :

Anatomically constrained CT image translation for heterogeneous blood vessel segmentation

(1, 2, 3) , (4) , (1, 5) , (6) , (7, 8) , (1, 2, 3) , (9)
1
2
3
4
5
6
7
8
9

Abstract

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).
Fichier principal
Vignette du fichier
0776.pdf (954.82 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03797472 , version 1 (04-10-2022)

Identifiers

  • HAL Id : hal-03797472 , version 1

Cite

Giammarco La Barbera, Haithem Boussaid, Francesco Maso, Sabine Sarnacki, Laurence Rouet, et al.. Anatomically constrained CT image translation for heterogeneous blood vessel segmentation. BMVC 2022 - The 33rd British Machine Vision Conference, Nov 2022, London, United Kingdom. ⟨hal-03797472⟩
4 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More