@article{lin2024usformer,
abstract = {Left atrial (LA) fibrosis plays a vital role as a mediator in the progression of atrial fibrillation. 3D late gadolinium-enhancement (LGE) MRI has been proven effective in identifying LA fibrosis. Image analysis of 3D LA LGE involves manual segmentation of the LA wall, which is both lengthy and challenging. Automated segmentation poses challenges owing to the diverse intensities in data from various vendors, the limited contrast between LA and surrounding tissues, and the intricate anatomical structures of the LA. Current approaches relying on 3D networks are computationally intensive since 3D LGE MRIs and the networks are large. Regarding this issue, most researchers came up with two-stage methods: initially identifying the LA center using a scaled-down version of the MRIs and subsequently cropping the full-resolution MRIs around the LA center for final segmentation. We propose a lightweight transformer-based 3D architecture, Usformer, designed to precisely segment LA volume in a single stage, eliminating error propagation associated with suboptimal two-stage training. The transposed attention facilitates capturing the global context in large 3D volumes without significant computation requirements. Usformer outperforms the state-of-the-art supervised learning methods in terms of accuracy and speed. First, with the smallest Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD), it achieved a dice score of 93.1% and 92.0% in the 2018 Atrial Segmentation Challenge and our local institutional dataset, respectively. Second, the number of parameters and computation complexity are largely reduced by 2.8x and 3.8x, respectively. Moreover, Usformer does not require a large dataset. When only 16 labeled MRI scans are used for training, Usformer achieves a 92.1% dice score in the challenge dataset. The proposed Usformer delineates the boundaries of the LA wall relatively accurately, which may assist in the clinical translation of LA LGE for planning catheter ablation of atrial fibrillation.},
author = {Lin, Hui and L{\'{o}}pez-Tapia, Santiago and Schiffers, Florian and Wu, Yunan and Gunasekaran, Suvai and Hwang, Julia and Bishara, Dima and Kholmovski, Eugene and Elbaz, Mohammed and Passman, Rod S. and Kim, Daniel and Katsaggelos, Aggelos K.},
doi = {10.1016/j.heliyon.2024.e28539},
issn = {24058440},
journal = {Heliyon},
keywords = {,3D LGE MRI,Attention,Deep learning,Image segmentation,Left atrium,Small dataset,Transformer},
number = {7},
publisher = {Elsevier},
title = {{Usformer: A small network for left atrium segmentation of 3D LGE MRI}},
volume = {10},
year = {2024}
}

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Current approaches relying on 3D networks are computationally intensive since 3D LGE MRIs and the networks are large. Regarding this issue, most researchers came up with two-stage methods: initially identifying the LA center using a scaled-down version of the MRIs and subsequently cropping the full-resolution MRIs around the LA center for final segmentation. We propose a lightweight transformer-based 3D architecture, Usformer, designed to precisely segment LA volume in a single stage, eliminating error propagation associated with suboptimal two-stage training. The transposed attention facilitates capturing the global context in large 3D volumes without significant computation requirements. Usformer outperforms the state-of-the-art supervised learning methods in terms of accuracy and speed. First, with the smallest Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD), it achieved a dice score of 93.1% and 92.0% in the 2018 Atrial Segmentation Challenge and our local institutional dataset, respectively. Second, the number of parameters and computation complexity are largely reduced by 2.8x and 3.8x, respectively. Moreover, Usformer does not require a large dataset. When only 16 labeled MRI scans are used for training, Usformer achieves a 92.1% dice score in the challenge dataset. The proposed Usformer delineates the boundaries of the LA wall relatively accurately, which may assist in the clinical translation of LA LGE for planning catheter ablation of atrial fibrillation.","author":[{"propositions":[],"lastnames":["Lin"],"firstnames":["Hui"],"suffixes":[]},{"propositions":[],"lastnames":["López-Tapia"],"firstnames":["Santiago"],"suffixes":[]},{"propositions":[],"lastnames":["Schiffers"],"firstnames":["Florian"],"suffixes":[]},{"propositions":[],"lastnames":["Wu"],"firstnames":["Yunan"],"suffixes":[]},{"propositions":[],"lastnames":["Gunasekaran"],"firstnames":["Suvai"],"suffixes":[]},{"propositions":[],"lastnames":["Hwang"],"firstnames":["Julia"],"suffixes":[]},{"propositions":[],"lastnames":["Bishara"],"firstnames":["Dima"],"suffixes":[]},{"propositions":[],"lastnames":["Kholmovski"],"firstnames":["Eugene"],"suffixes":[]},{"propositions":[],"lastnames":["Elbaz"],"firstnames":["Mohammed"],"suffixes":[]},{"propositions":[],"lastnames":["Passman"],"firstnames":["Rod","S."],"suffixes":[]},{"propositions":[],"lastnames":["Kim"],"firstnames":["Daniel"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]}],"doi":"10.1016/j.heliyon.2024.e28539","issn":"24058440","journal":"Heliyon","keywords":",3D LGE MRI,Attention,Deep learning,Image segmentation,Left atrium,Small dataset,Transformer","number":"7","publisher":"Elsevier","title":"Usformer: A small network for left atrium segmentation of 3D LGE MRI","volume":"10","year":"2024","bibtex":"@article{lin2024usformer,\nabstract = {Left atrial (LA) fibrosis plays a vital role as a mediator in the progression of atrial fibrillation. 3D late gadolinium-enhancement (LGE) MRI has been proven effective in identifying LA fibrosis. Image analysis of 3D LA LGE involves manual segmentation of the LA wall, which is both lengthy and challenging. Automated segmentation poses challenges owing to the diverse intensities in data from various vendors, the limited contrast between LA and surrounding tissues, and the intricate anatomical structures of the LA. Current approaches relying on 3D networks are computationally intensive since 3D LGE MRIs and the networks are large. Regarding this issue, most researchers came up with two-stage methods: initially identifying the LA center using a scaled-down version of the MRIs and subsequently cropping the full-resolution MRIs around the LA center for final segmentation. We propose a lightweight transformer-based 3D architecture, Usformer, designed to precisely segment LA volume in a single stage, eliminating error propagation associated with suboptimal two-stage training. The transposed attention facilitates capturing the global context in large 3D volumes without significant computation requirements. Usformer outperforms the state-of-the-art supervised learning methods in terms of accuracy and speed. First, with the smallest Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD), it achieved a dice score of 93.1% and 92.0% in the 2018 Atrial Segmentation Challenge and our local institutional dataset, respectively. Second, the number of parameters and computation complexity are largely reduced by 2.8x and 3.8x, respectively. Moreover, Usformer does not require a large dataset. When only 16 labeled MRI scans are used for training, Usformer achieves a 92.1% dice score in the challenge dataset. 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