Multi-Level Contextual Network for Biomedical Image Segmentation

TL;DR

This paper introduces a novel deep convolutional residual network with a unique skip connection strategy for biomedical image segmentation, effectively capturing local and global context while maintaining computational efficiency.

Contribution

The proposed end-to-end network architecture integrates multi-level contextual information with a new skip connection approach, improving segmentation accuracy and efficiency.

Findings

Achieves accurate pixel-wise segmentation on public datasets

Uses fewer parameters for computational efficiency

Provides fast and reliable biomedical image segmentation

Abstract

Accurate and reliable image segmentation is an essential part of biomedical image analysis. In this paper, we consider the problem of biomedical image segmentation using deep convolutional neural networks. We propose a new end-to-end network architecture that effectively integrates local and global contextual patterns of histologic primitives to obtain a more reliable segmentation result. Specifically, we introduce a deep fully convolution residual network with a new skip connection strategy to control the contextual information passed forward. Moreover, our trained model is also computationally inexpensive due to its small number of network parameters. We evaluate our method on two public datasets for epithelium segmentation and tubule segmentation tasks. Our experimental results show that the proposed method provides a fast and effective way of producing a pixel-wise dense prediction of biomedical images.