Original paper

HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images

Volume: 213, Pages: 106501 - 106501
Published: Jan 1, 2022
Abstract
Automatic liver and tumor segmentation are essential steps to take decisive action in hepatic disease detection, deciding therapeutic planning, and post-treatment assessment. The computed tomography (CT) scan has become the choice of medical experts to diagnose hepatic anomalies. However, due to advancements in CT image acquisition protocol, CT scan data is growing and manual delineation of the liver and tumor from the CT volume becomes...
Paper Details
Title
HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images
Published Date
Jan 1, 2022
Volume
213
Pages
106501 - 106501
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