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An attention-based dense network model for cardiac image segmentation using learning approaches

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Abstract

Although the outcomes of the DL techniques obtained are promising, performance is always limited to some extent due to a need for more sufficient data. We observe the viability of using DL techniques on a test data set in this study. We introduce an easy-to-use method for performing 2D slice-by-slice segmentation based on Region of Interest (ROI). This method employs an improved training system to enhance segmentation. Here, a novel attention-based U-Net architecture (\(a-{\text{UNet}}\)) is used. Additionally, by examining our methodology on two separate sets of data, the imATFIB set of data and the openly accessible ACDC challenge belonging to our internal medical research, the combination of both the attention and U-Net model shows utterly different architectures and yields consistent advantages. ATFIB dataset outcomes demonstrate that the suggested method works well with the practice extents offered, averaging 89.89% on the validation set, Dice Similarity Coefficient of the entire heart. Additionally, with a mean Dice score of 91.87% on the ACDC validation, our algorithm ranked as the second-best strategy for the challenge. Our method offers the chance to act as a component of a software-aided diagnosis in medicine.

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Authors and Affiliations

  1. Department Data Science and Business System, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    Nandhagopal Subaramani & E. Sasikala

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  1. Nandhagopal Subaramani
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  2. E. Sasikala
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Correspondence to Nandhagopal Subaramani.

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Subaramani, N., Sasikala, E. An attention-based dense network model for cardiac image segmentation using learning approaches. Soft Comput 28, 765–775 (2024). https://doi.org/10.1007/s00500-023-09482-1

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