GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images

Uslu, Fatmatulzehra

doi:10.1016/j.compmedimag.2023.102266

Computerized Medical Imaging and Graphics

2023

DOI: 10.1016/j.compmedimag.2023.102266

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GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images

Fatmatulzehra Uslu

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2024

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An Evidential Mask Transformer for Left Atrium Segmentation

Uslu

2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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The segmentation of the left atrium (LA) is required to calculate the clinical parameters of the LA, to identify diseases related to its remodeling. Generally, convolutional networks have been used for this task. However, their performance may be limited as a result of the use of local convolution operations for feature extraction. Also, such models usually need extra steps to provide uncertainty maps such as multiple forward passes for Monte Carlo dropouts or training multiple models for ensemble learning. To address these issues, we adapt mask transformers for LA segmentation which effectively use both local and global information, and train them with evidential learning to generate uncertainty maps from the learned Dirichlet distribution, with a single forward pass. We validated our approach on the STACOM 2013 dataset and found that our method can produce better segmentation performance than baseline models, and can identify locations our model’s responses are not trustable.

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An Evidential Mask Transformer for Left Atrium Segmentation

Uslu

2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images

Cited by 1 publication

References 25 publications

An Evidential Mask Transformer for Left Atrium Segmentation

An Evidential Mask Transformer for Left Atrium Segmentation

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