Pulmonary Fissure Segmentation in CT Images Based on ODoS Filter and Shape Features

TL;DR

This paper introduces a novel pulmonary fissure segmentation method in CT images using an ODoS filter combined with shape and orientation features, achieving high accuracy on public datasets.

Contribution

The study presents a new segmentation approach that integrates ODoS filtering, orientation partitioning, and shape analysis to improve fissure detection in challenging CT images.

Findings

Median F1-score of 0.896 on LOLA11 dataset

False Discovery Rate of 0.109

False Negative Rate of 0.100

Abstract

Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung diseases. In CT images, pulmonary fissure segmentation is a formidable mission due to various of factors. To address the challenge, an useful approach based on ODoS filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by merging the orientation information and magnitude information to highlight structure features for fissure enhancement, which can effectively distinguish between pulmonary fissures and clutters. Motivated by the fact that pulmonary fissures appear as linear structures in 2D space and planar structures in 3D space in orientation field, an orientation curvature criterion and an orientation partition scheme are fused to separate fissure patches and other structures in different orientation partition, which can suppress parts of clutters. Considering the shape difference between pulmonary fissures and tubular structures in magnitude field, a shape measure approach and a 3D skeletonization model are combined to segment pulmonary fissures for clutters removal. When applying our scheme to 55 chest CT scans which acquired from a publicly available LOLA11 datasets, the median F1-score, False Discovery Rate (FDR), and False Negative Rate (FNR) respectively are 0.896, 0.109, and 0.100, which indicates that the presented method has a satisfactory pulmonary fissure segmentation performance.