LiM-Net: Lightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images

•Computed Tomography (CT) is a non-invasive technique commonly utilized to diagnose hepatic anomalies.•Automatic liver segmentation from CT images plays a decisive role in numerous medical procedures and treatment strategies that deal with liver complications.•The deep learning-based lightweight multi-level multiscale approach with deep residual learning enhances the automatic liver segmentation performance. In addition, it reduces the number of parameters and the computational complexity of the network.•The proposed heuristic modifications in the network design extract the multi-level multiscale features that boost the network's learning potential and generalization capability.•The effectiveness of the proposed method is confirmed on publicly available 3DIRCADb, CHAOS and LiTS CT datasets. Automatic liver segmentation gained significant attention in the medical realm to deal with liver anomalies. Furthermore, due to advancements in medical imaging, data volume is increasing day-to-day, which seeks the demand for automatic liver segmentation techniques to evade the labour-intensive process of liver delineation currently followed by medical experts. The proposed method is based on the deep learning approach. We exploited the multi-level multiscale feature extraction and fusion concept to uplift the liver segmentation outcome. The computationally efficient pre-activated multiscale Res2Net backbone architecture with channel-wise attention (PARCA) block plugged into the Unet++ architecture to extract the multiscale fine-grained features with refinement and dense skip connections used for the multiscale feature fusion from the various stages of the network. As a result, the fine-grained multiscale features and multiscale feature fusion from diverse stages provide rich contextual feature representation that enhances decoder competence. Further, we optimized the network using a custom loss function that handles the class imbalance and focuses on the complicated samples from the dataset. The efficacy of the proposed lightweight model was tested experimentally using the publicly available 3DIRCADb, CHAOS and LiTS CT datasets. The proposed model achieved the DSC of 97.3%, 95.1%, and 96.3% on the 3DIRCADb, CHAOS, and LiTS datasets. Nevertheless, the proposed network is lightweight and has 7.5 million parameters which are less than the classical Unet and Unet++ architecture. Thus, the proposed heuristics uplift the liver segmentation outcome and significantly reduce t