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 acceptable segmentation results of liver tumor. In this paper, we design an Attention Hybrid Connection Network (AHCNet) architecture which combines soft and hard attention mechanism and long and short skip connections. We also propose a cascade network based on 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 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 110 cases in the LiTS dataset and extensively evaluated by the 20 cases in the 3DIRCADb dataset and 117 cases in a Clinical dataset, which indicates that the proposed method can achieve faster network convergence and accurate semantic segmentation, and further demonstrate the proposed method has good clinical value.

Objective: Based on the hypothesis that adding a cross-modal and cross-attention (C ² MA) mechanism into a deep learning network improves accuracy and efficacy of medical image segmentation, we propose to test a novel network to segment acute ischemic stroke (AIS) lesions from four CT perfusion (CTP) maps. Methods: The proposed network uses a C ² MA module directly to establish a spatial-wise relationship by using the multigroup non-local attention operation between two modal features and performs dynamic group-wise recalibration through group attention block. This C ² MA-Net has a multipath encoder-decoder architecture, in which each modality is processed in different streams on the encoding path, and the pair related parameter modalities are used to bridge attention across multimodal information through the C ² MA module. A public dataset involving 94 training and 62 test cases are used to build and evaluate the C ² MA-Net. AIS segmentation results on testing cases are analyzed and compared with other state-of-the-art models reported in the literature. Results: By calculating several average evaluation scores, C ² MA-network improves Recall and F2 scores by 6% and 1%, respectively. In the ablation experiment, the F1 score of C ² MA-Net is at least 7.8% higher than that of single-input single-modal self-attention networks. Conclusion: This study demonstrates advantages of applying C ² MA-network to segment AIS lesions, which yields promising segmentation accuracy, and achieves semantic decoupling by processing different parameter modalities separately. Significance: Proving the potential of cross-modal interactions in attention to assist identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies.

Liver cancer is one of the most common cancers. Liver tumor segmentation is one of the most important steps in treating liver cancer. Accurate tumor segmentation on computed tomography (CT) images is a challenge task due to the variation of tumor’s shape, size and location. To this end, this paper proposes a liver tumor segmentation method on CT volumes using multi-scale candidate generation method (MCG), 3D fractal residual network (3D FRN), and active contour model (ACM) in a coarse-to-fine manner. First, livers are segmented using 3D U-Net and then MCG is performed on these liver regions for obtaining tumor candidates (all superpixel blocks). Second, 3D FRN is proposed to further determine tumor regions, which is considered as coarse segmentation results. Finally, ACM is used for tumor segmentation refinement. The proposed 3D MCG-FRN+ACM is trained using the 110 cases in the LiTS dataset and evaluated on a public liver tumor dataset of 3DIRCADb with dice per case of 0.67. The experimentations and comparisons demonstrate the performance advantage of 3D MCG-FRN+ACM compared to other segmentation methods.