FCoDT-Net: A Novel Framework for High-Precision Medical Image Segmentation Using Contextual Distillation Transformer

Abstract

Current methods in medical image semantic segmentation often rely on simple skip connections within U-shaped network structures. These approaches fail to bridge the semantic gap between the encoder and decoder module and do not fully exploit the rich contextual information among. The unused information leads to suboptimal segmentation results. In this paper, we propose the Feature Context Distillation Transformer Network (FCoDT-Net), a deep learning model designed to address these limitations by leveraging the rich contextual information within the skip connections. FCoDT-Net introduces a novel Context Distillation Transformer (CoDT) within its decoder module, which effectively utilizes abundant contextual information from skip connections. CoDT leverages attention matrix calculations between feature maps at similar scales to exploit rich context information. The narrowed semantic gap then can generate features which can be more efficiently utilized by the decoder. Further, we integrate Convolutional Network Next(ConvNeXt) block into our encoder module to enhance feature extraction capabilities. Experimental simulations show that our FCoDT-Net excels in both 3D and 2D applications. In 3D, FCoDT-Net achieves exceptional results on the lung dataset, with a Dice score of 0.9250 for airway segmentation. In 2D, FCoDT-Net demonstrates state-of-the-art performance on both the Gland Segmentation (Glas) and Multi-Organ Nuclei Segmentation and Classification (MoNuSeg) datasets.