AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes

Abstract

The liver is a common site for the development of primary (i.e., originating from the liver, e.g., hepatocellular carcinoma) or secondary (i.e., spread to the liver, e.g., colorectal cancer) tumor. Due to its complex background, heterogeneous, and diffusive shape, automatic segmentation of tumor remains a challenging task. So far, only the interactive method has been adopted to obtain the acceptable segmentation results of a liver tumor. In this paper, we design an Attention Hybrid Connection Network architecture which combines soft and hard attention mechanism and long and short skip connections. We also propose a cascade network based on the liver localization network, liver segmentation network, and tumor segmentation network to cope with this challenge. Simultaneously, the joint dice loss function is proposed to train the liver localization network to obtain the accurate 3D liver bounding box, and the focal binary cross entropy is used as a loss function to fine-tune the tumor segmentation network for detecting more potentially malignant tumor and reduce false positives. Our framework is trained using the 110 cases in the LiTS dataset and extensively evaluated by the 20 cases in the 3DIRCADb dataset and the 117 cases in the Clinical dataset, which indicates that the proposed method can achieve faster network convergence and accurate semantic segmentation and further demonstrate that the proposed method has a good clinical value.INDEX TERMS Liver tumor segmentation, deep convolutional neural network, feature fusion, attention mechanism.

Automatic segmentation of kidney and liver tumors in CT images

“…We deliberately selected an off-the-shelf CNN (LinkNet-34), which was not the state-of-the-art network. Consistently applying different kinds of techniques, we have reached competitive results and outperformed at least one compound CNN [22] for liver and liver tumor segmentations.…”

“…In 2019, Jiang et. al [22] proposed a 3D FCN structure, composed of multiple Attention Hybrid Connection Blocks (AHCBlocks) densely connected with both long and short skip connections and soft self-attention modules. Same training process with LiTS and 3DIRCADb datasets estimated DICE 95.9% and 73.4% for liver and tumor segmentation accordingly.…”

A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT

“…Li et al [17] combined a 2D DenseUnet network that extracted the intra-slice features and the 3D counterpart for hierarchically aggregating volumetric contexts, for liver and lesion segmentation. Jiang et al [18] used a cascade structure to segment the liver tumor. They combined the soft and hard attention mechanisms, long and short skip connections.…”

Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

“…The main limitations of these methods are that they need manuallycreated initial regions of interest (ROIs), and there is still room for improvement in terms of segmentation accuracy. Recently, deep learning has been applied in liver tumor segmentation from CT images [30]- [34]. For example, Li et al proposed a novel H-DenseUNet by combining a 2D U-net and a 3D convolutional neural network (CNN) [16].…”

“…Seo et al proposed a modified U-net by adding a residual path to the skip connection parts of U-net [33]. Jiang et al presented a CNN structure making use of attention mechanism and skip connections [34]. The main limitation of deep learning techniques is that they do not incorporate domain knowledge and may suffer uninterpretable segmentation errors.…”