Simple and robust depth-wise cascaded network for polyp segmentation

The segmentation of the polyp region in colonoscopy images is considered difficult due to size, texture, and color variation. To segment polyps successfully, models based on convolutional neural networks (CNN), transformers, and their combinations have been developed. However, these methods are limited in that they can only model the local appearance of polyps or lack multi-level feature representation for spatial dependency in the decoding process. In this paper, we propose a simple, efficient yet powerful polyp segmentation framework that unifies the network with a multiscale cascaded path. The proposed MMS-Net utilizes multiscale and multipath convolutional operations in conjunction with multiple deep feature aggregation. The overall dense empowered features are sufficient for pixel-by-pixel detection of the polyp region. Extensive experiments on two popular benchmark datasets for polyp segmentation (Kvasir and CVC-Clinic DB) and two datasets of other medical applications (DRIVE and MC) are presented. The results show that our MMS-Net performs comparably to or better than other state-of-the-art methods despite having two or even three orders of magnitude fewer trainable parameters. [Display omitted] •Powerful polyp segmentation framework that unifies a multiscale cascaded path.•Dual-function boosters (FBs) are used to extract small size features.•MMS-Net employs three dense skip paths for the transfer of edge information.•Proposed MMS-Net outperforms other state-of-the-art methods.•MMS-Net used two or even three orders of magnitude fewer trainable parameters.