Semi-automatic vessel detection for challenging cases of peripheral arterial disease
Gabriel Mistelbauer1, Anca Morar2, Rüdiger Schernthaner3
1Department of Simulation and Graphics, Otto-von-Guericke University Magdeburg, Germany.
Insights
This study introduces a faster semi-automatic method for segmenting lower extremity arteries in Peripheral Arterial Disease (PAD) patients. The novel approach significantly reduces processing time while maintaining high accuracy for clinical use.
Area of Science:
- Medical Imaging
- Vascular Biology
- Machine Learning in Medicine
Background:
- Manual segmentation of lower extremity arteries in Peripheral Arterial Disease (PAD) is challenging due to disease complexity and imaging artifacts.
- Existing methods are time-consuming and hinder the development of automated solutions requiring large annotated datasets.
Purpose of the Study:
- To develop a semi-automatic vessel tracking approach to accelerate the creation of annotated training data for PAD.
- To reduce manual interaction and processing time for lower extremity arterial tree segmentation.
Main Methods:
- A novel semi-automatic vessel tracking algorithm was developed.
- The approach automatically classifies vessels, bones, and tissues, then tracks and organizes vessels into a tree structure.
- Validation was performed through pilot (N=9) and clinical (N=24) studies.
Main Results:
- The approach achieved high accuracy in identifying clinically relevant lower extremity arteries, with 92.9% sensitivity and 99.9% specificity/overall accuracy.
- Expert physicians could readily identify all relevant arteries, even in complex cases.
- The method successfully segmented challenging cases involving discontinuities, calcified plaque, and imaging artifacts.
Conclusions:
- The proposed semi-automatic method significantly reduces segmentation time by 39% compared to current clinical workflows.
- This approach facilitates faster creation of annotated training data, crucial for advancing machine learning in PAD research.
- The technique demonstrates clinical readiness for efficient and accurate lower extremity arterial tree analysis.
Objectives:
Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Peripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifestations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data.
Methods:
We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically classifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization.
Results:
We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufficient quality for clinical application, with our current clinically established workflow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average specificity and overall accuracy of 99.9%.
Conclusions:
Compared to the clinical workflow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.
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