A lightweight neural network with multiscale feature enhancement for liver CT segmentation

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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Bibliographic Details
Main Author: Mohammed Yusuf Ansari (16904523) (author)
Other Authors: Yin Yang (35103) (author), Shidin Balakrishnan (14150580) (author), Julien Abinahed (14151792) (author), Abdulla Al-Ansari (14150583) (author), Mohamed Warfa (18282250) (author), Omran Almokdad (18282253) (author), Ali Barah (14777533) (author), Ahmed Omer (18282256) (author), Ajay Vikram Singh (204056) (author), Pramod Kumar Meher (17316988) (author), Jolly Bhadra (14147823) (author), Osama Halabi (14158905) (author), Mohammad Farid Azampour (18282259) (author), Nassir Navab (6254012) (author), Thomas Wendler (13175656) (author), Sarada Prasad Dakua (14151789) (author)
Published: 2022
Subjects:
Biomedical and clinical sciences
Clinical sciences
Engineering
Biomedical engineering
Information and computing sciences
Artificial intelligence
Machine learning
Abdominal Computed Tomography (CT)
Segmentation
Visceral organ diseases
Hepatocellular carcinoma
Neural network
Res-PAC-UNet
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