A lightweight neural network with multiscale feature enhancement for liver CT segmentation
<p dir="ltr">Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backb...
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| مؤلفون آخرون: | , , , , , , , , , , , , , , , |
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2022
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| الملخص: | <p dir="ltr">Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.</p><p dir="ltr">Publisher Correction: A lightweight neural network with multiscale feature enhancement for liver CT segmentation: <a href="https://dx.doi.org/10.1038/s41598-022-20472-5" target="_blank">https://dx.doi.org/10.1038/s41598-022-20472-5</a>, published online 21 September 2022.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-16828-6" target="_blank">https://dx.doi.org/10.1038/s41598-022-16828-6</a></p> |
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