Key result
A proposed image segmentation method achieved Dice scores of 90.73% and 92.05% for left atrial structures on the LASC2013 and ASC2018 datasets, respectively.
Why the study?
Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for AF diagnosis and robotic surgery treatment, but faces challenges from 3D to 2D sequence conversion and varying scales across slices.
The proposed multi-scale feature fusion and sequence relationship learning method achieves high accuracy in segmenting the left atrium from cardiac MRI, which may aid in AF diagnosis and robotic surgery planning.
May aid left atrial segmentation in CMR for AF; leaves open clinical adoption pending prospective validation.
Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network's ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.
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Qi et al. (2023) studied Atrial fibrillation. Image segmentation method based on sequence relationship learning and multi-scale feature fusion was evaluated on Dice, IoU, and Hausdorff distance values. A proposed image segmentation method achieved Dice scores of 90.73% and 92.05% for left atrial structures on the LASC2013 and ASC2018 datasets, respectively.
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