DFDM: A Deep Feature Decoupling Module for Lung Nodule Segmentation

Abstract

In this paper, we propose a novel feature decoupling method to tackle two critical problems in the lung nodule segmentation task: (i) ambiguity of nodule boundary leads to the imprecise segmentation boundary and (ii) the high false positive rate of segmentation result. Our motivation is that an accurate segmentation network needs explicitly modeling the nodule boundary and texture information, and suppressing the noise information. To do so, a novel Deep Feature Decoupling Module (DFDM) is proposed to decouple the nodule boundary, noise, and texture information from the original feature maps. The decoupled boundary and texture information is used to benefit the segmentation, and the noise information is removed from the input features to reduce the false positive rate. The proposed DFDM consists of three parallel branches, including Boundary Sensitive Branch (BSB), Noise Removal Branch (NRB), and Texture Preserving Branch (TPB) to decouple the mentioned three information, respectively. In particular, we design our BSB with a novel architecture to effectively capture the boundary information of lung nodules. We apply the proposed DFDM to the U-Net architecture and achieve convincing segmentation results on the LIDC-IDRI dataset. Code and models are available at https://github.com/chinichenw/DFDM.

Warp‐based edge feature reinforcement network for medical image segmentation

“…proposed decoupled architecture. Chen et al 15 . outputs the features of different frequencies using convolution layers with different dilation rates and then extracts the edges by subtraction.…”

“…Li et al 14 proposed decoupled architecture. Chen et al 15 outputs the features of different frequencies using convolution layers with different dilation rates and then extracts the edges by subtraction. Kuang et al 16 extracted edges by subtracting the body estimate from the original feature.…”