A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome

Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.

Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection

This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed to identify the four key bifurcation points. Then, based on the detected bifurcation points, the entire airway tree is reconstructed by a new region-growing method. Finally, with the basic airway tree anatomy and topology knowledge, individual branches of the airway tree are classified into different categories in terms of pulmonary lobes. There are several advantages in our method such as the detection of the bifurcation points does not depend on the segmentation of airway tree and only four bifurcation points need to be manually labeled for each sample to prepare the training dataset. The segmentation of airway tree is guided by the detected points, which overcomes the difficulty of manual seed selection of conventional region-growing algorithm. In addition, the bifurcation points can help analyze the tree structure, which provides a basis for effective airway tree labeling. Experimental results show that our method is fast, stable, and the accuracy of our method is 97.85%, which is higher than that of the traditional skeleton-based method. Graphical Abstract The pipeline of our proposed lobe-based airway tree labeling method. Given a raw CT volume, a neural network structure is designed to predict major bifurcation points of airway tree. Based on the detected points, airway tree is reconstructed and labeled in terms of lobes.