Dense Dilated Deep Multiscale Supervised U-Network for biomedical image segmentation

Biomedical image segmentation is essential for computerized medical image analysis. Deep learning algorithms allow us to design state-of-the-art models for solving segmentation problems. The U-Net and its variants have provided positive results across various datasets. However, the existing networks have the same receptive field at each level and the models are supervised only at the shallow level. Considering these two ideas, we have proposed the D3MSU-Net where the field of view in each level is varied depending upon the depth of the resolution layer and the model is supervised at each resolution level. We have evaluated our network in eight benchmark datasets such as Electron Microscopy, Lung segmentation, Montgomery Chest X-ray, Covid-Radiopaedia, Wound, Medetec, Brain MRI, and Covid-19 lung CT dataset. Additionally, we have provided the performance for various ablations. The experimental results show the superiority of the proposed network. The proposed D3MSU-Net and ablation models are available at www.github.com/shirshabose/D3MSUNET.

Automated summary

Biomedical image segmentation is crucial for medical image analysis, and deep learning algorithms allow for the design of advanced models to solve segmentation problems. The D3MSU-Net is proposed, which varies the receptive field at each level based on the resolution layer's depth and performs supervision at each resolution level. Experimental results demonstrate the superiority of the proposed network.