Abstract
One critical research area in the development of a computer-aided diagnosis system for liver cancer is efficient and automatic segmentation of lesion from CT scans. To overcome this issue, we investigated a 2.5D lightweight liver tumor segmentation by fusing the multi-scale semantic features, named MAA-Net. Our framework enhanced the information interaction between the input 2.5D stacked slice via introducing parallel convolution and increasing the knowledge weight of the lesion channel in different receptive fields. To ease the shortage of missed detection of tumors, MAA-Net fused the hierarchical semantic information extracted from the encoder. Moreover, we evaluated our MAA-Net on LiTS2017 and 3DIRCADb datasets. Extensive experiments shows the proposed method outperforms the others on both accuracy and total number of calculation. Specifically, our approach can improve liver tumor segmentation tasks by 2.4%, while reducing amount of parameters by 57.5%. Both quantitative and qualitative results illustrated the MAA-Net can effectively address with the limitation of small tumors, and some tumors are on the edge.
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You, Y., Bai, Z., Zhang, Y., Du, J. (2023). 2.5D Lightweight Network Integrating Multi-scale Semantic Features for Liver Tumor Segmentation. In: Su, R., Zhang, Y., Liu, H., F Frangi, A. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2022. Lecture Notes in Electrical Engineering, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-16-6775-6_14
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