Segmenting Hepatic Lesions Using Residual Attention U-Net with an Adaptive Weighted Dice Loss
Journal
Proceedings - International Conference on Image Processing, ICIP
Journal Volume
2019-September
ISBN
9781538662496
Date Issued
2019-09-01
Author(s)
Abstract
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.
Subjects
attention module | CT image segmentation | hepatic lesion factor | residual block
Type
conference paper