Automated volumetric assessment of hepatocellular carcinomas using multi-phase-fused dual-attention network

Objectives Assessing patients’ responses to Transarterial Chemoembolization (TACE) treatment by analyzing the volume changes of Hepatocellular Carcinomas (HCC) is critical for treatment planning and prognosis. To obtain the precise cancer volumes in real time and assess its responses efficiently, an automated segmentation network is proposed for HCCs and extensively evaluated in this study. Methods To tackle the low image contrast in the single phase CT images, a network based on the fusion of the multi-phase images and dual-attention mechanism (MFDA-Net) is developed. Despite the U-Net-based backbone network, the Multi-Phase Feature Fusion (MPFF) and Dual-Attention Mechanism (DAM) are proposed to fuse the semantic features from different phases and enhance the spatial and channel-wise discriminative features, respectively. To automate the assessment of the HCC patients’ responses, the correlations between the cancer volumes produced by the segmentation results and the manual delineations were deeply analyzed using diverse metrics. Results Six metrics were employed for the quantitative evaluation of the proposed MFDA-Net on a multi-phase abdominal CT dataset. Furthermore, Response Evaluation Criteria in Solid Tumors (RECIST) and volumetric RECIST (vRECIST) were utilized to analyze the cancer volumes. Experimental results demonstrated that the proposed MFDA-Net outperforms the other twelve comparison algorithms significantly. Meanwhile, the segmentation results have strong statistical correlations with the manual delineations on both of the RECIST and vRECIST. Conclusions Extensive experiments validated that the proposed MFDA-Net is an optional method compared with the labor-intensive manual delineations to assess the HCC patients’ responses after the treatments. The source code is available at https://github.com/mqy-git111/MFDA-Net .