標題: SEGMENTING HEPATIC LESIONS USING RESIDUAL ATTENTION U-NET WITH AN ADAPTIVE WEIGHTED DICE LOSS
作者: Liu, Yu-Cheng
Tan, Daniel Stanley
Chen, Jyh-Cheng
Cheng, Wen-Huang
Hua, Kai-Lung
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: CT image segmentation;residual block;attention module;hepatic lesion factor
公開日期: 1-一月-2019
摘要: We propose a novel network architecture called Residual Attention U-Net (ResAttU-Net) for segmenting hepatic lesions. Our model incorporates residual blocks that can extract more complex features as compared with traditional convolutional layers combined with a skip-connection attention module that learns to focus on the relevant features for the task of hepatic lesions segmentation. Moreover, we train our model using an adaptive weighted dice loss that prioritizes the pixels of the tumor class over the pixels of the background class. We evaluate our model on the MICCAI Liver Tumor Segmentation (LiTS) benchmark dataset. Our experimental results show that our method significantly improves upon several state-of-the-art baselines for hepatic lesion or liver tumor segmentation.
URI: http://hdl.handle.net/11536/154049
ISBN: 978-1-5386-6249-6
ISSN: 1522-4880
期刊: 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
起始頁: 3322
結束頁: 3326
顯示於類別:會議論文