Abstract
Radio imaging has become instrumental in examining internal anomalies and body organs. The medical expert manually delineates the images in routine practice and reaches the decision. However, manual intervention leads to errors due to human limitations, and the decision becomes operator-dependent. Therefore, automatic liver segmentation from computed tomography (CT) images plays a decisive role in detecting hepatic anomalies, treatment planning, liver transplantation, liver cancer treatment, and post-treatment assessment. Recently deep learning (DL) techniques have proven competence in medical image segmentation. The proposed method is DL-based multiscale feature fusion with channel-wise attention network (MFCA-Net) designed using the computationally efficient Res2Net (R2N) layer, which has the ability to enhance the receptive field of convolutional neural network (CNN) and extract the multiscale information at a more granular level.
Further, we reconstructed the multiscale low-level features and fused them with high-level features that enhance the semantic details in the features. In addition, we employed the channel-wise attention mechanism that renovates the features by modelling the interdependencies between the channels and focusing on the prominent features. Also, we altered the low-level features by fusing the renovated features that augment the contextual information, which simplifies the network learning potential. The efficacy of the proposed network was demonstrated on the CHAOS challenge test dataset, where the network attained the DICE of 96.67 ± 0.72%. Thus, the proposed MFCA-Net is computationally efficient, and liver segmentation performance is comparable with state-of-the-art methods.
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The authors would like to thank the Faculty and Management of VPKBIET, Baramati, and VIIT, Baramati, for enabling the essential resources to accomplish the proposed research work.
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Kushnure, D.T., Talbar, S.N. (2022). MFCA-Net: Multiscale Feature Fusion with Channel-Wise Attention Network for Automatic Liver Segmentation from CT Images. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_10
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