R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation

• DOI: 10.1007/s00521-022-07419-7
• Publication Date: 2022-06-03T17:02:37Z
• Authors: Mehreen Mubashar; Hazrat Ali; Christer Grönlund; Shoaib Azmat
• Keywords: Feature (linguistics); Convolution (computer science); Semantic gap

Abstract U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by imaging community, performance suffers on complicated datasets. The problem can be ascribed to simple feature extracting blocks: encoder/decoder, and semantic gap between encoder decoder. Variants (such as R2U-Net) have been proposed address blocks making deeper, but it does not deal with problem. On other hand, another variant UNET++ deals introducing dense skip connections has extraction blocks. To overcome these issues, we propose new based segmentation architecture R2U++. In architecture, adapted changes from vanilla are: (1) plain convolutional backbone replaced deeper recurrent residual convolution block. increased field view aids crucial features for which proven improvement overall network. (2) decoder reduced pathways. These pathways accumulate coming multiple scales apply concatenation accordingly. modified embedded multi-depth models, an ensemble outputs taken varying depths improves foreground objects appearing at various images. R2U++ evaluated four distinct modalities: electron microscopy, X-rays, fundus, computed tomography. average gain achieved IoU score 1.5 ± 0.37% dice 0.9 0.33% over UNET++, whereas, 4.21 2.72 3.47 1.89 R2U-Net across different