Vision Transformers increase efficiency of 3D cardiac CT multi-label
segmentation
- URL: http://arxiv.org/abs/2310.09099v2
- Date: Wed, 24 Jan 2024 14:33:32 GMT
- Title: Vision Transformers increase efficiency of 3D cardiac CT multi-label
segmentation
- Authors: Lee Jollans, Mariana Bustamante, Lilian Henriksson, Anders Persson,
Tino Ebbers
- Abstract summary: Two cardiac computed tomography (CT) datasets were used to train networks to segment multiple regions representing the whole heart in 3D.
The segmented regions included the left and right atrium and ventricle, left ventricular myocardium, ascending aorta, pulmonary arteries, pulmonary veins, and left atrial appendage.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Accurate segmentation of the heart is essential for personalized blood flow
simulations and surgical intervention planning. Segmentations need to be
accurate in every spatial dimension, which is not ensured by segmenting data
slice by slice. Two cardiac computed tomography (CT) datasets consisting of 760
volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from
60 patients respectively were used to train networks to simultaneously segment
multiple regions representing the whole heart in 3D. The segmented regions
included the left and right atrium and ventricle, left ventricular myocardium,
ascending aorta, pulmonary arteries, pulmonary veins, and left atrial
appendage. The widely used 3D U-Net and the UNETR architecture were compared to
our proposed method optimized for large volumetric inputs. The proposed network
architecture, termed Transformer Residual U-Net (TRUNet), maintains the cascade
downsampling encoder, cascade upsampling decoder and skip connections from
U-Net, while incorporating a Vision Transformer (ViT) block in the encoder
alongside a modified ResNet50 block. TRUNet reached higher segmentation
performance for all structures within approximately half the training time
needed for 3D U-Net and UNETR. The proposed method achieved more precise vessel
boundary segmentations and better captured the heart's overall anatomical
structure compared to the other methods. The fast training time and accurate
delineation of adjacent structures makes TRUNet a promising candidate for
medical image segmentation tasks. The code for TRUNet is available at
github.com/ljollans/TRUNet.
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